Big Data Basics - Part 1 - Introduction to Big Data

Big Data Basics - Part 1 - Introduction to Big Data

Problem

I have been hearing the term Big Data for a while now and would like to know more about it. Can you explain what this term means, how it evolved, and how we identify Big Data and any other relevant details?

Solution

Big Data has been a buzz word for quite some time now and it is catching popularity faster than pretty much anything else in the technology world. In this tip, let us understand what this buzz word is all about, what is its significance, why you should care about it, and more.

What is Big Data?

Wikipedia defines "Big Data" as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
In simple terms, "Big Data" consists of very large volumes of heterogeneous data that is being generated, often, at high speeds. These data sets cannot be managed and processed using traditional data management tools and applications at hand. Big Data requires the use of a new set of tools, applications and frameworks to process and manage the data.

Evolution of Data / Big Data

Data has always been around and there has always been a need for storage, processing, and management of data, since the beginning of human civilization and human societies. However, the amount and type of data captured, stored, processed, and managed depended then and even now on various factors including the necessity felt by humans, available tools/technologies for storage, processing, management, effort/cost, ability to gain insights into the data, make decisions, and so on.
Going back a few centuries, in the ancient days, humans used very primitive ways of capturing/storing data like carving on stones, metal sheets, wood, etc. Then with new inventions and advancements a few centuries in time, humans started capturing the data on paper, cloth, etc. As time progressed, the medium of capturing/storage/management became punching cards followed by magnetic drums, laser disks, floppy disks, magnetic tapes, and finally today we are storing data on various devices like USB Drives, Compact Discs, Hard Drives, etc.
In fact the curiosity to capture, store, and process the data has enabled human beings to pass on knowledge and research from one generation to the next, so that the next generation does not have to re-invent the wheel.
As we can clearly see from this trend, the capacity of data storage has been increasing exponentially, and today with the availability of the cloud infrastructure, potentially one can store unlimited amounts of data. Today Terabytes and Petabytes of data is being generated, captured, processed, stored, and managed.

Characteristics of Big Data - The Three V's of Big Data

When do we say we are dealing with Big Data? For some people 1TB might seem big, for others 10TB might be big, for others 100GB might be big, and something else for others. This term is qualitative and it cannot really be quantified. Hence we identify Big Data by a few characteristics which are specific to Big Data. These characteristics of Big Data are popularly known as Three V's of Big Data.
The three v's of Big Data are Volume, Velocity, and Variety as shown below.

Volume

Volume refers to the size of data that we are working with. With the advancement of technology and with the invention of social media, the amount of data is growing very rapidly. This data is spread across different places, in different formats, in large volumes ranging from Gigabytes to Terabytes, Petabytes, and even more. Today, the data is not only generated by humans, but large amounts of data is being generated by machines and it surpasses human generated data. This size aspect of data is referred to as Volume in the Big Data world.

Velocity

Velocity refers to the speed at which the data is being generated. Different applications have different latency requirements and in today's competitive world, decision makers want the necessary data/information in the least amount of time as possible. Generally, in near real time or real time in certain scenarios. In different fields and different areas of technology, we see data getting generated at different speeds. A few examples include trading/stock exchange data, tweets on Twitter, status updates/likes/shares on Facebook, and many others. This speed aspect of data generation is referred to as Velocity in the Big Data world.

Variety

Variety refers to the different formats in which the data is being generated/stored. Different applications generate/store the data in different formats. In today's world, there are large volumes of unstructured data being generated apart from the structured data getting generated in enterprises. Until the advancements in Big Data technologies, the industry didn't have any powerful and reliable tools/technologies which can work with such voluminous unstructured data that we see today. In today's world, organizations not only need to rely on the structured data from enterprise databases/warehouses, they are also forced to consume lots of data that is being generated both inside and outside of the enterprise like clickstream data, social media, etc. to stay competitive. Apart from the traditional flat files, spreadsheets, relational databases etc., we have a lot of unstructured data stored in the form of images, audio files, video files, web logs, sensor data, and many others. This aspect of varied data formats is referred to as Variety in the Big Data world.

Sources of Big Data

Just like the data storage formats have evolved, the sources of data have also evolved and are ever expanding. There is a need for storing the data into a wide variety of formats. With the evolution and advancement of technology, the amount of data that is being generated is ever increasing. Sources of Big Data can be broadly classified into six different categories as shown below.

Enterprise Data

There are large volumes of data in enterprises in different formats. Common formats include flat files, emails, Word documents, spreadsheets, presentations, HTML pages/documents, pdf documents, XMLs, legacy formats, etc. This data that is spread across the organization in different formats is referred to as Enterprise Data.

Transactional Data

Every enterprise has some kind of applications which involve performing different kinds of transactions like Web Applications, Mobile Applications, CRM Systems, and many more. To support the transactions in these applications, there are usually one or more relational databases as a backend infrastructure. This is mostly structured data and is referred to as Transactional Data.

Social Media

This is self-explanatory. There is a large amount of data getting generated on social networks like Twitter, Facebook, etc. The social networks usually involve mostly unstructured data formats which includes text, images, audio, videos, etc. This category of data source is referred to as Social Media.

Activity Generated

There is a large amount of data being generated by machines which surpasses the data volume generated by humans. These include data from medical devices, censor data, surveillance videos, satellites, cell phone towers, industrial machinery, and other data generated mostly by machines. These types of data are referred to as Activity Generated data.

Public Data

This data includes data that is publicly available like data published by governments, research data published by research institutes, data from weather and meteorological departments, census data, Wikipedia, sample open source data feeds, and other data which is freely available to the public. This type of publicly accessible data is referred to as Public Data.

Archives

Organizations archive a lot of data which is either not required anymore or is very rarely required. In today's world, with hardware getting cheaper, no organization wants to discard any data, they want to capture and store as much data as possible. Other data that is archived includes scanned documents, scanned copies of agreements, records of ex-employees/completed projects, banking transactions older than the compliance regulations. This type of data, which is less frequently accessed, is referred to as Archive Data.

Formats of Data

Data exists in multiple different formats and the data formats can be broadly classified into two categories - Structured Dataand Unstructured Data.
Structured data refers to the data which has a pre-defined data model/schema/structure and is often either relational in nature or is closely resembling a relational model. Structured data can be easily managed and consumed using the traditional tools/techniques. Unstructured data on the other hand is the data which does not have a well-defined data model or does not fit well into the relational world.
Structured data includes data in the relational databases, data from CRM systems, XML files etc. Unstructured data includes flat files, spreadsheets, Word documents, emails, images, audio files, video files, feeds, PDF files, scanned documents, etc.

Big Data Statistics

• 100 Terabytes of data is uploaded to Facebook every day
• Facebook Stores, Processes, and Analyzes more than 30 Petabytes of user generated data
• Twitter generates 12 Terabytes of data every day
• LinkedIn processes and mines Petabytes of user data to power the "People You May Know" feature
• YouTube users upload 48 hours of new video content every minute of the day
• Decoding of the human genome used to take 10 years. Now it can be done in 7 days
• 500+ new websites are created every minute of the day
• Source: Wikibon - A Comprehensive List of Big Data Statistics

In this tip we were introduced to Big Data, how it evolved, what are its primary characteristics, what are the sources of data, and a few statistics showing how large volumes of heterogeneous data is being generated at different speeds.

References

• http://en.m.wikipedia.org/wiki/Big_data
• http://strata.oreilly.com/2012/01/what-is-big-data.html

Next Steps

• Explore more about Big Data. Do some of your own searches to see what you can find.
• Stay tuned for future tips in this series to learn more about the Big Data ecosystem.

Cloudera Certification Practice Test

Cloudera Certification Practice Test Exam with answers

1. You have decided to develop a custom composite key class that you will use for keys emitted during the map phase to be consumed by a reducer. Which interface must this key implement?
Correct Answer: WritableComparable

Explanation

All keys and values must implement the Writable interface so that they can be wriiten to disk. In addition, keys emitted during the map phase must implement WritableComparable so that the keys can be sorted during the shuffle and sort phase. WritableComparable inherits from Writable.

Further Reading

• For more information, see the Yahoo! Developer Network Apach Hadoop Tutorial, Custom Key Types.
• Hadoop: the Definitive Guide, chapter four, in the Serialization: The Writable Interface section.

2. You’ve built a MapReduce job that denormalizes a very large table, resulting in an extremely large amount of output data. Which two cluster resources will your job stress? (Choose two).
Correct Answer: network I/O , disk I/O

Explanation

When denormalizing a table, the amount of data written by the map phase will far exceed the amount of data read by the map phase. All of the data written during the map phase is first written to local disk and then transferred over the network to the reducers during the beginning of the reduce phase. Writing a very large amount of data in the map phase will therefore create a large amount of local disk I/O on the machines running map tasks and network I/O. Because map output is stored in a fixed size buffer that is written periodically to disk, this operation will not tax the memory of the machines running the map tasks. Denormalizing a table is not a compute-intesive operation, so this operation will not tax the processors of the machines running the map tasks.

Further Reading

• Hadoop: the Definitive Guide, chapter six, Shuffle and Sort: The Map Side section includes more information on the process for writing map output to disk.
• Hadoop: the Definitive Guide, chapter six, Shuffle and Sort: The Reduce Side section explains further how data is transferred to the reducers
• Denormalizing a table is a form of join operation. You can read more about performing joins in MapReduce in Join Algorithms using Map/Reduce

3. When is the earliest that the reduce() method of any reduce task in a given job called?
Correct Answer: Not until all map tasks have completed

Explanation

No reduce task&rquo;s reduce() method is called until all map tasks have completed. Every reduce task&rquo;s reduce() method expects to receive its data in sorted order. If the reduce() method is called before all of the map tasks have completed, it would be possible that the reduce() method would receive the data out of order.

Further Reading

• Hadoop: The Definitive Guide, chapter six includes a detailed explanation of the shuffle and sort phase of a MapReduce job.

4. You have 50 files in the directory /user/foo/example. Each file is 300MB. You submit a MapReduce job with /user/foo/example as the input path.

How much data does a single Mapper processes as this job executes?

Correct Answer: A single input split

Explanation

An input split is a unit of work (a chunk of data) that is processed by a single map task in a MapReduce program (represented by the Java interface InputSplit). The InputFormat you specify for MapReduce program determines how input files are split into records and read. Each map task processes a single split; each split is further comprised of records (key-value pairs) which the map task processes.

A MapReduce program run over a data set is usually called a MapReduce “job.” By splitting up input files, MapReduce can process a single file in parallel; if the file is very large, this represents a significant performance improvement. Also, because a single file is worked on in splits, it allows MapReduce to schedule those processes on different nodes in the cluster, nodes that have that piece of data already locally stored on that node, which also results in significant performance improvements. An InputSplit can span HDFS block boundaries.

Further Reading

• Hadoop: The Definitive Guide, chapter two includes an excellent general discussion of input splits

Hadoop Administrator

5. In the execution of a MapReduce job, where does the Mapper place the intermediate data of each Map task?

Correct Answer: The Mapper stores the intermediate data on the underlying filesystem of the local disk of the machine which ran the Map task

Explanation

Intermediate data from a Mapper is stored on the local filesystem of the machine on which the Mapper ran. Each Reducer then copies its portion of that intermediate data to its own local disk. When the job terminates, all intermediate data is removed.

• See Hadoop: The Definitive Guide, 3rd Edition, Chapter 2, under the section "Data Flow"

6. A client wishes to read a file from HDFS. To do that, it needs the block IDs (their names) which make up the file. From where does it obtain those block IDs?
Correct Answer: The NameNode reads the block IDs from memory and returns them to the client.

Explanation

When a client wishes to read a file from HDFS, it contacts the NameNode and requests the names and locations of the blocks which make up the file. For rapid access, the NameNode has the block IDs stored in RAM.

Further Reading

See Hadoop Operations, under the section "The Read Path."

7. Your cluster has slave nodes in three different racks, and you have written a rack topology script identifying each machine as being in rack1, rack2, or rack3. A client machine outside of the cluster writes a small (one-block) file to HDFS. The first replica of the block is written to a node on rack2. How is block placement determined for the other two replicas?
Correct Answer: Either both will be written to nodes on rack1, or both will be written to nodes on rack3.

Explanation

For the default threefold replication, Hadoop’s rack placement policy is to write the first copy of a block on a node in one rack, then the other two copies on two nodes in a different rack. Since the first copy is written to rack2, the other two will either be written to two nodes on rack1, or two nodes on rack3.

Further Reading

• For more information on rack topology and block placement policy, see Hadoop Operations: A Guide for Developers and Administrators page 130.
• See the Apache Docs on Data Replication

8. A slave node in your cluster has 24GB of RAM, and 12 physical processor cores on hyperthreading-enabled processors. You set the value of mapred.child.java.opts to -Xmx1G, and the value of mapred.tasktracker.map.tasks.maximum to 12. What is the most appropriate value to set for mapred.tasktracker.reduce.tasks.maximum?
Correct Answer: 6

Explanation

For optimal performance, it is important to avoid the use of virtual memory (swapping) on slave nodes. From the configuration shown, the slave node could run 12 Map tasks simultaneously, and each one will use 1GB of RAM, resulting in a total of 12GB used. The TaskTracker daemon itself will use 1GB of RAM, as will the DataNode daemon. This is a total of 14GB. The operating system will also use some RAM -- a reasonable estimate would be 1-2GB. Thus, we can expect to have approximately 8-9GB of RAM available for Reducers. So the most appropriate of the choices presented is that we should configure the node to be able to run 6 Reducers simultaneously.

Further Reading

Hadoop: The Definitive Guide, 3rd Edition, Chapter 9, under the section “Environment Settings”
HBase

9. Your client application needs to scan a region for the row key value 104. Given a store file that contains the following list of Row Key values:

100, 101, 102, 103, 104, 105, 106, 107 What would a bloom filter return?
Correct Answer: Confirmation that 104 may be contained in the set

Explanation

A Bloom filter is a kind of membership test using probability -- it tells you whether an element is a member of a set. It is quick and memory-efficient. The trade-off is that it is probabilistic where false positives are possible though false negatives are not; thus if your Bloom Filter returns true, it confirms that a key may be contained in a table. If Bloom Filter returns false, it confirms that a key is definitely not contained in a table.

Enabling Bloom Filters may save your disk seek and improve read latency.

Further Reading

• Wikipedia on Bloom Filters
• HBase Documentation on Bloom Filters section 12.6.4. Bloom Filters includes:

Bloom Filters can be enabled per ColumnFamily. Use HColumnDescriptor.setBloomFilterType(NONE | ROW | ROWCOL) to enable blooms per ColumnFamily. Default = NONE for no bloom filters. If ROW, the hash of the row will be added to the bloom on each insert. If ROWCOL, the hash of the row + column family + column family qualifier will be added to the bloom on each key insert.

10. You have two tables in an existing RDBMS. One contains information about the products you sell (name, size, color, etc.) The other contains images of the products in JPEG format. These tables are frequently joined in queries to your database. You would like to move this data into HBase. What is the most efficient schema design for this scenario?
Correct Answer: Create a single table with two column families

Explanation

Access patterns are an important factor in HBase schema design. Even though the two tables in this scenario have very different data sizes and formats, it is better to store them in one table if you are accessing them together most of the time.

Column families allow for separation of data. You can store different types of data and format into different column families. Attributes such as compression, Bloom filters, and replication are set on per column family basis. In this example, it is better to store product information and product images into two different column families and one table.

Further Reading

HBase Documentation on Column Family Section 5.6.especially the part:

Physically, all column family members are stored together on the filesystem. Because tunings and storage specifications are done at the column family level, it is advised that all column family members have the same general access pattern and size characteristics.

11. You need to create a Blogs table in HBase. The table will consist of a single Column Family calledContent and two column qualifiers, Author and Comment. What HBase shell command should you use to create this table?
Correct Answer: create 'Blogs', 'Content'

Explanation

When you create a HBase table, you need to specify table name and column family name.
In the Hbase Shell, you can create a table with the command:
create 'myTable', 'myColumnFamily'

For this example:

• Table name: 'Blogs'
• ColumnFamily: 'Content'

We can create the table and verify it with describe table command.
hbase> create 'Blogs', 'Content' hbase> describe 'Blogs' {Name => 'Blogs', FAMILIES => [{NAME => 'Content', ....

Further Reading

see the HBase Shell commands for create
​
Create table; pass table name, a dictionary of specifications per column family, and optionally a dictionary of table configuration. Dictionaries are described below in the GENERAL NOTES section.
Examples:
hbase> create 't1', {NAME => 'f1', VERSIONS => 5} hbase> create 't1', {NAME => 'f1'}, {NAME => 'f2'}, {NAME => 'f3'} hbase> # The above in shorthand would be the following: hbase> create 't1', 'f1', 'f2', 'f3' hbase> create 't1', {NAME => 'f1', VERSIONS => 1, TTL => 2592000, BLOCKCACHE => true}

12. From within an HBase application you are performing a check and put operation with the following code:table.checkAndPut(Bytes.toBytes("rowkey"),Bytes.toBytes("colfam"),Bytes.toBytes("qualifier"), Bytes.toBytes("barvalue"), newrow));

Which describes this check and put operation?

Correct Answer: Check if rowkey/colfam/qualifier exists and has the cell value "barvalue". If so, put the values in newrow and return "true".

Explanation

The method checkAndPut returns "true" if a row with specific column family, and column qualifier value matches the expected value; if "true" is returned, it executes the put with new value. If the specific value is not present in a row, it returns "false" and the put is not executed.

Further Reading

HBase Documentation on checkAndPut:

Data Science Essentials (DS-200)

13. What is the best way to evaluate the quality of the model found by an unsupervised algorithm likek-means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?
Correct Answer: The lowest cost clustering subject to a stability constraint

Explanation

There is a tradeoff between cost and stability in unsupervised learning. The more tightly you fit the data, the less stable the model will be, and vice versa. The idea is to find a good balance with more weight given to the cost. Typically a good approach is to set a stability threshold and select the model that achieves the lowest cost above the stability threshold.

• Yale Algorithms in Data Mining - Lecture 10: k-means clustering
• Evaluation of Stability of k-means Cluster Ensembles with Respect to Random Initialization

14. A sandwich shop studies the number of men, and women, that enter the shop during the lunch hour from noon to 1pm each day. They find that the number of men that enter can be modeled as a random variable with distribution Poisson(M), and likewise the number of women that enter asPoisson(W). What is likely to be the best model of the total number of customers that enter during the lunch hour?
Correct Answer: Poisson(M+W)

Explanation

The total number of customers is the sum of the number of men and women. The sum of two Poisson random variables also follows a Poisson distribution with rate equal to the sum of their rates. The Normal and Binomial distribution can approximate the Poisson distribution in certain cases, but the expressions above do not approximate Poisson(M+W).

15. Which condition supports the choice of a support vector machine over logistic regression for a classification problem?
Correct Answer: The test set will be dense, and contain examples close to decision boundary learned from the training set

Explanation

The SVM algorithm is a maximum margin classifier, and tries to pick a decision boundary that creates the widest margin between classes, rather than just any boundary that separates the classes. This helps generalization to test data, since it is less likely to misclassify points near the decision boundary, as the boundary maintains a large margin from training examples.

SVMs are not particularly better at multi-label clasification. Linear separability is not required for either classification technique, and does not relate directly to an advantage of SVMs. SVMs are not particularly more suited to low dimensional data.

Hadoop HDFS Interview Questions

What is BIG DATA?

Big Data is nothing but an assortment of such a huge and complex data that it becomes very tedious to capture, store, process, retrieve and analyze it with the help of on-hand database management tools or traditional data processing techniques.

To know more about BIG DATA, browse through The Hype Behind Big Data!

Can you give some examples of Big Data?

There are many real life examples of Big Data! Facebook is generating 500+ terabytes of data per day, NYSE (New York Stock Exchange) generates about 1 terabyte of new trade data per day, a jet airline collects 10 terabytes of censor data for every 30 minutes of flying time. All these are day to day examples of Big Data!

Can you give a detailed overview about the Big Data being generated by Facebook?

As of December 31, 2012, there are 1.06 billion monthly active users on facebook and 680 million mobile users. On an average, 3.2 billion likes and comments are posted every day on Facebook. 72% of web audience is on Facebook. And why not! There are so many activities going on facebook from wall posts, sharing images, videos, writing comments and liking posts, etc. In fact, Facebook started using Hadoop in mid-2009 and was one of the initial users of Hadoop.

According to IBM, what are the three characteristics of Big Data?

According to IBM, the three characteristics of Big Data are:
Volume: Facebook generating 500+ terabytes of data per day.
Velocity: Analyzing 2 million records each day to identify the reason for losses.
Variety: images, audio, video, sensor data, log files, etc.

How Big is ‘Big Data’?

With time, data volume is growing exponentially. Earlier we used to talk about Megabytes or Gigabytes. But time has arrived when we talk about data volume in terms of terabytes, petabytes and also zettabytes! Global data volume was around 1.8ZB in 2011 and is expected to be 7.9ZB in 2015. It is also known that the global information doubles in every two years!

How analysis of Big Data is useful for organizations?

Effective analysis of Big Data provides a lot of business advantage as organizations will learn which areas to focus on and which areas are less important. Big data analysis provides some early key indicators that can prevent the company from a huge loss or help in grasping a great opportunity with open hands! A precise analysis of Big Data helps in decision making! For
instance, nowadays people rely so much on Facebook and Twitter before buying any product or service. All thanks to the Big Data explosion.

Who are ‘Data Scientists’?

Data scientists are soon replacing business analysts or data analysts. Data scientists are experts who find solutions to analyze data. Just as web analysis, we have data scientists who have good business insight as to how to handle a business challenge. Sharp data scientists are not only involved in dealing business problems, but also choosing the relevant issues that can bring value addition to the organization.

What is Hadoop?

Hadoop is a framework that allows for distributed processing of large data sets across clusters of commodity computers using a simple programming model.

Why the name ‘Hadoop’?

Hadoop doesn’t have any expanding version like ‘oops’. The charming yellow elephant you see is basically named after Doug’s son’s toy elephant!

Why do we need Hadoop?

Everyday a large amount of unstructured data is getting dumped into our machines. The major challenge is not to store large data sets in our systems but to retrieve and analyze the big data in the organizations, that too data present in different machines at different locations. In this situation a necessity for Hadoop arises. Hadoop has the ability to analyze the data present in different machines at different locations very quickly and in a very cost effective way. It uses the concept of MapReduce which enables it to divide the query into small parts and process them in parallel. This is also known as parallel computing.

What are some of the characteristics of Hadoop framework?

Hadoop framework is written in Java. It is designed to solve problems that involve analyzing large data (e.g. petabytes). The programming model is based on Google’s MapReduce. The infrastructure is based on Google’s Big Data and Distributed File System. Hadoop handles large files/data throughput and supports data intensive distributed applications. Hadoop is scalable as more nodes can be easily added to it.
Give a brief overview of Hadoop history.
In 2002, Doug Cutting created an open source, web crawler project.
In 2004, Google published MapReduce, GFS papers.
In 2006, Doug Cutting developed the open source, Mapreduce and HDFS project.
In 2008, Yahoo ran 4,000 node Hadoop cluster and Hadoop won terabyte sort benchmark.
In 2009, Facebook launched SQL support for Hadoop.

Give examples of some companies that are using Hadoop structure?

A lot of companies are using the Hadoop structure such as Cloudera, EMC, MapR, Hortonworks, Amazon, Facebook, eBay, Twitter, Google and so on.

What is the basic difference between traditional RDBMS and Hadoop?

Traditional RDBMS is used for transactional systems to report and archive the data, whereas Hadoop is an approach to store huge amount of data in the distributed file system and process it. RDBMS will be useful when you want to seek one record from Big data, whereas, Hadoop will be useful when you want Big data in one shot and perform analysis on that later.

What is structured and unstructured data?

Structured data is the data that is easily identifiable as it is organized in a structure. The most common form of structured data is a database where specific information is stored in tables, that is, rows and columns. Unstructured data refers to any data that cannot be identified easily. It could be in the form of images, videos, documents, email, logs and random text. It is not in the form of rows and columns.

What are the core components of Hadoop?

Core components of Hadoop are HDFS and MapReduce. HDFS is basically used to store large data sets and MapReduce is used to process such large data sets.

What is HDFS?

HDFS is a file system designed for storing very large files with streaming data access patterns, running clusters on commodity hardware.

What are the key features of HDFS?

HDFS is highly fault-tolerant, with high throughput, suitable for applications with large data sets, streaming access to file system data and can be built out of commodity hardware.

What is Fault Tolerance?

Suppose you have a file stored in a system, and due to some technical problem that file gets destroyed. Then there is no chance of getting the data back present in that file. To avoid such situations, Hadoop has introduced the feature of fault tolerance in HDFS. In Hadoop, when we store a file, it automatically gets replicated at two other locations also. So even if one or two of the systems collapse, the file is still available on the third system.

Replication causes data redundancy then why is is pursued in HDFS?

HDFS works with commodity hardware (systems with average configurations) that has high chances of getting crashed any time. Thus, to make the entire system highly fault-tolerant, HDFS replicates and stores data in different places. Any data on HDFS gets stored at atleast 3 different locations. So, even if one of them is corrupted and the other is unavailable for some time for any reason, then data can be accessed from the third one. Hence, there is no chance of losing the data. This replication factor helps us to attain the feature of Hadoop called Fault Tolerant.

Since the data is replicated thrice in HDFS, does it mean that any calculation done on one node will also be replicated on the other two?

Since there are 3 nodes, when we send the MapReduce programs, calculations will be done only on the original data. The master node will know which node exactly has that particular data. In case, if one of the nodes is not responding, it is assumed to be failed. Only then, the required calculation will be done on the second replica.

What is throughput? How does HDFS get a good throughput?

Throughput is the amount of work done in a unit time. It describes how fast the data is getting accessed from the system and it is usually used to measure performance of the system. In HDFS, when we want to perform a task or an action, then the work is divided and shared among different systems. So all the systems will be executing the tasks assigned to them independently and in parallel. So the work will be completed in a very short period of time. In this way, the HDFS gives good throughput. By reading data in parallel, we decrease the actual time to read data tremendously.

What is streaming access?

As HDFS works on the principle of ‘Write Once, Read Many‘, the feature of streaming access is extremely important in HDFS. HDFS focuses not so much on storing the data but how to retrieve it at the fastest possible speed, especially while analyzing logs. In HDFS, reading the complete data is more important than the time taken to fetch a single record from the data.

What is a commodity hardware? Does commodity hardware include RAM?

Commodity hardware is a non-expensive system which is not of high quality or high-availability.
Hadoop can be installed in any average commodity hardware. We don’t need super computers or high-end hardware to work on Hadoop. Yes, Commodity hardware includes RAM because there will be some services which will be running on RAM.

What is a Namenode?

Namenode is the master node on which job tracker runs and consists of the metadata. It maintains and manages the blocks which are present on the datanodes. It is a high-availability machine and single point of failure in HDFS.

Is Namenode also a commodity?

No. Namenode can never be a commodity hardware because the entire HDFS rely on it. It is the single point of failure in HDFS. Namenode has to be a high-availability machine.

What is a metadata?

Metadata is the information about the data stored in datanodes such as location of the file, size of the file and so on.

What is a Datanode?

Datanodes are the slaves which are deployed on each machine and provide the actual storage. These are responsible for serving read and write requests for the clients.

Why do we use HDFS for applications having large data sets and not when there are lot of small files?

HDFS is more suitable for large amount of data sets in a single file as compared to small amount of data spread across multiple files. This is because Namenode is a very expensive high performance system, so it is not prudent to occupy the space in the Namenode by unnecessary amount of metadata that is generated for multiple small files. So, when there is a large amount of data in a single file, name node will occupy less space. Hence for getting optimized performance, HDFS supports large data sets instead of multiple small files.

What is a daemon?

Daemon is a process or service that runs in background. In general, we use this word in UNIX environment. The equivalent of Daemon in Windows is “services” and in Dos is ” TSR”.

What is a job tracker?

Job tracker is a daemon that runs on a namenode for submitting and tracking MapReduce jobs in Hadoop. It assigns the tasks to the different task tracker. In a Hadoop cluster, there will be only one job tracker but many task trackers. It is the single point of failure for Hadoop and MapReduce Service. If the job tracker goes down all the running jobs are halted. It receives heartbeat from task tracker based on which Job tracker decides whether the assigned task is
completed or not.

What is a task tracker?

Task tracker is also a daemon that runs on datanodes. Task Trackers manage the execution of individual tasks on slave node. When a client submits a job, the job tracker will initialize the job and divide the work and assign them to different task trackers to perform MapReduce tasks.While performing this action, the task tracker will be simultaneously communicating with job tracker by sending heartbeat. If the job tracker does not receive heartbeat from task tracker within specified time, then it will assume that task tracker has crashed and assign that task to another task tracker in the cluster.

Is Namenode machine same as datanode machine as in terms of hardware?

It depends upon the cluster you are trying to create. The Hadoop VM can be there on the same machine or on another machine. For instance, in a single node cluster, there is only one machine,whereas in the development or in a testing environment, Namenode and datanodes are on different machines.

What is a heartbeat in HDFS?

A heartbeat is a signal indicating that it is alive. A datanode sends heartbeat to Namenode and task tracker will send its heart beat to job tracker. If the Namenode or job tracker does not receive heart beat then they will decide that there is some problem in datanode or task tracker is unable to perform the assigned task.

Are Namenode and job tracker on the same host?

No, in practical environment, Namenode is on a separate host and job tracker is on a separate host.

What is a ‘block’ in HDFS?

A ‘block’ is the minimum amount of data that can be read or written. In HDFS, the default block size is 64 MB as contrast to the block size of 8192 bytes in Unix/Linux. Files in HDFS are broken down into block-sized chunks, which are stored as independent units. HDFS blocks are large as compared to disk blocks, particularly to minimize the cost of seeks.

If a particular file is 50 mb, will the HDFS block still consume 64 mb as the default size?

No, not at all! 64 mb is just a unit where the data will be stored. In this particular situation, only 50 mb will be consumed by an HDFS block and 14 mb will be free to store something else. It is the MasterNode that does data allocation in an efficient manner.

What are the benefits of block transfer?

A file can be larger than any single disk in the network. There’s nothing that requires the blocks from a file to be stored on the same disk, so they can take advantage of any of the disks in the cluster. Making the unit of abstraction a block rather than a file simplifies the storage subsystem. Blocks provide fault tolerance and availability. To insure against corrupted blocks and disk and machine failure, each block is replicated to a small number of physically separate machines (typically three). If a block becomes unavailable, a copy can be read from another location in a way that is transparent to the client.

If we want to copy 10 blocks from one machine to another, but another machine can copy only 8.5 blocks, can the blocks be broken at the time of replication?

In HDFS, blocks cannot be broken down. Before copying the blocks from one machine to another, the Master node will figure out what is the actual amount of space required, how many block are being used, how much space is available, and it will allocate the blocks accordingly.

How indexing is done in HDFS?

Hadoop has its own way of indexing. Depending upon the block size, once the data is stored, HDFS will keep on storing the last part of the data which will say where the next part of the data will be. In fact, this is the base of HDFS.

If a data Node is full how it’s identified?

When data is stored in datanode, then the metadata of that data will be stored in the Namenode. So Namenode will identify if the data node is full.

If datanodes increase, then do we need to upgrade Namenode?

While installing the Hadoop system, Namenode is determined based on the size of the clusters.

Most of the time, we do not need to upgrade the Namenode because it does not store the actual data, but just the metadata, so such a requirement rarely arise.

Are job tracker and task trackers present in separate machines?

Yes, job tracker and task tracker are present in different machines. The reason is job tracker is a single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted.

When we send a data to a node, do we allow settling in time, before sending another data to that node?

Yes, we do.

Does hadoop always require digital data to process?

Yes. Hadoop always require digital data to be processed.

On what basis Namenode will decide which datanode to write on?

As the Namenode has the metadata (information) related to all the data nodes, it knows which datanode is free.

Doesn’t Google have its very own version of DFS?

Yes, Google owns a DFS known as “Google File System (GFS)” developed by Google Inc. for its own use.

Who is a ‘user’ in HDFS?

A user is like you or me, who has some query or who needs some kind of data.

Is client the end user in HDFS?

No, Client is an application which runs on your machine, which is used to interact with the Namenode (job tracker) or datanode (task tracker).

What is the communication channel between client and namenode/datanode?

The mode of communication is SSH.

What is a rack?

Rack is a storage area with all the datanodes put together. These datanodes can be physically located at different places. Rack is a physical collection of datanodes which are stored at a single location. There can be multiple racks in a single location.

On what basis data will be stored on a rack?

When the client is ready to load a file into the cluster, the content of the file will be divided into blocks. Now the client consults the Namenode and gets 3 datanodes for every block of the file which indicates where the block should be stored. While placing the datanodes, the key rule followed is “for every block of data, two copies will exist in one rack, third copy in a different rack“. This rule is known as “Replica Placement Policy“.

Do we need to place 2nd and 3rd data in rack 2 only?

Yes, this is to avoid datanode failure.

What if rack 2 and datanode fails?

If both rack2 and datanode present in rack 1 fails then there is no chance of getting data from it.In order to avoid such situations, we need to replicate that data more number of times instead of replicating only thrice. This can be done by changing the value in replication factor which is set to 3 by default.

What is a Secondary Namenode? Is it a substitute to the Namenode?

The secondary Namenode constantly reads the data from the RAM of the Namenode and writes it into the hard disk or the file system. It is not a substitute to the Namenode, so if the Namenode fails, the entire Hadoop system goes down. This is called Hadoop Single Point Of Failure (SPOF)

What is the difference between Gen1 and Gen2 Hadoop with regards to the Namenode?

In Gen 1 Hadoop, Namenode is the single point of failure. In Gen 2 Hadoop, we have what is known as Active and Passive Namenodes kind of a structure. If the active Namenode fails, passive Namenode takes over the charge.

What is MapReduce?

Map Reduce is the ‘heart‘ of Hadoop that consists of two parts – ‘map’ and ‘reduce’. Maps and reduces are programs for processing data. ‘Map’ processes the data first to give some intermediate output which is further processed by ‘Reduce’ to generate the final output.

Thus, MapReduce allows for distributed processing of the map and reduction operations.

Can you explain how do ‘map’ and ‘reduce’ work?

Namenode takes the input and divide it into parts and assign them to data nodes. These datanodes process the tasks assigned to them and make a key-value pair and returns the intermediate output to the Reducer. The reducer collects this key value pairs of all the datanodes and combines them and generates the final output.

What is ‘Key value pair’ in HDFS?

Key value pair is the intermediate data generated by maps and sent to reduces for generating the final output.

What is the difference between MapReduce engine and HDFS cluster?

HDFS cluster is the name given to the whole configuration of master and slaves where data is stored. Map Reduce Engine is the programming module which is used to retrieve and analyze data.

Is map like a pointer?

No, Map is not like a pointer.

Do we require two servers for the Namenode and the datanodes?

Yes, we need two different servers for the Namenode and the datanodes. This is because Namenode requires highly configurable system as it stores information about the location details of all the files stored in different datanodes and on the other hand, datanodes require low configuration system.

Why are the number of splits equal to the number of maps?

The number of maps is equal to the number of input splits because we want the key and value pairs of all the input splits.

Is a job split into maps?

No, a job is not split into maps. Spilt is created for the file. The file is placed on datanodes in blocks. For each split, a map is needed.

Which are the two types of ‘writes’ in HDFS?

There are two types of writes in HDFS: posted and non-posted write. Posted Write is when we write it and forget about it, without worrying about the acknowledgement. It is similar to our traditional Indian post. In a Non-posted Write, we wait for the acknowledgement. It is similar to the today’s courier services. Naturally, non-posted write is more expensive than the posted write.

It is much more expensive, though both writes are asynchronous.

Why ‘Reading‘ is done in parallel and ‘Writing‘ is not in HDFS?

Reading is done in parallel because by doing so we can access the data fast. But we do not perform the write operation in parallel. The reason is that if we perform the write operation in parallel, then it might result in data inconsistency. For example, you have a file and two nodes are trying to write data into the file in parallel, then the first node does not know what the second node has written and vice-versa. So, this makes it confusing which data to be stored and accessed.

Can Hadoop be compared to NOSQL database like Cassandra?

Though NOSQL is the closet technology that can be compared to Hadoop, it has its own pros and cons. There is no DFS in NOSQL. Hadoop is not a database. It’s a filesystem (HDFS) and distributed programming framework (MapReduce).

Hadoop admin interview question and answers

Which operating system(s) are supported for production Hadoop deployment?

The main supported operating system is Linux. However, with some additional software Hadoop can be deployed on Windows.

What is the role of the namenode?

The namenode is the "brain" of the Hadoop cluster and responsible for managing the distribution blocks on the system based on the replication policy. The namenode also supplies the specific addresses for the data based on the client requests.

What happen on the namenode when a client tries to read a data file?

The namenode will look up the information about file in the edit file and then retrieve the remaining information from filesystem memory snapshot. Since the namenode needs to support a large number of the clients, the primary namenode will only send information back for the data location. The datanode itselt is responsible for the retrieval.

What are the hardware requirements for a Hadoop cluster (primary and secondary namenodes and datanodes)?

There are no requirements for datanodes. However, the namenodes require a specified amount of RAM to store filesystem image in memory Based on the design of the primary namenode and secondary namenode, entire filesystem information will be stored in memory. Therefore, both namenodes need to have enough memory to contain the entire filesystem image.

What mode(s) can Hadoop code be run in?

Hadoop can be deployed in stand alone mode, pseudo-distributed mode or fully-distributed mode. Hadoop was specifically designed to be deployed on multi-node cluster. However, it also can be deployed on single machine and as a single process for testing purposes

How would an Hadoop administrator deploy various components of Hadoop in production?

Deploy namenode and jobtracker on the master node, and deploy datanodes and taskstrackers on multiple slave nodes. There is a need for only one namenode and jobtracker on the system. The number of datanodes depends on the available hardware

What is the best practice to deploy the secondary namenodeDeploy secondary namenode on a separate standalone machine. The secondary namenode needs to be deployed on a separate machine. It will not interfere with primary namenode operations in this way. The secondary namenode must have the same memory requirements as the main namenode.

Is there a standard procedure to deploy Hadoop?

No, there are some differences between various distributions. However, they all require that Hadoop jars be installed on the machine. There are some common requirements for all Hadoop distributions but the specific procedures will be different for different vendors since they all have some degree of proprietary software

What is the role of the secondary namenode?

Secondary namenode performs CPU intensive operation of combining edit logs and current filesystem snapshots. The secondary namenode was separated out as a process due to having CPU intensive operations and additional requirements for metadata back-up

What are the side effects of not running a secondary name node?

The cluster performance will degrade over time since edit log will grow bigger and bigger. If the secondary namenode is not running at all, the edit log will grow significantly and it will slow the system down. Also, the system will go into safemode for an extended time since the namenode needs to combine the edit log and the current filesystem checkpoint image.

What happen if a datanode loses network connection for a few minutes?

The namenode will detect that a datanode is not responsive and will start replication of the data from remaining replicas. When datanode comes back online, the extra replicas will be The replication factor is actively maintained by the namenode. The namenode monitors the status of all datanodes and keeps track which blocks are located on that node. The moment the datanode is not avaialble it will trigger replication of the data from the existing replicas. However, if the datanode comes back up, overreplicated data will be deleted. Note: the data might be deleted from the original datanode.

What happen if one of the datanodes has much slower CPU?

The task execution will be as fast as the slowest worker. However, if speculative execution is enabled, the slowest worker will not have such big impact Hadoop was specifically designed to work with commodity hardware. The speculative execution helps to offset the slow workers. The multiple instances of the same task will be created and job tracker will take the first result into consideration and the second instance of the task will be killed.

What is speculative execution?

If speculative execution is enabled, the job tracker will issue multiple instances of the same task on multiple nodes and it will take the result of the task that finished first. The other instances of the task will be killed.

The speculative execution is used to offset the impact of the slow workers in the cluster. The jobtracker creates multiple instances of the same task and takes the result of the first successful task. The rest of the tasks will be discarded.

How many racks do you need to create an Hadoop cluster in order to make sure that the cluster operates reliably?

In order to ensure a reliable operation it is recommended to have at least 2 racks with rack placement configured Hadoop has a built-in rack awareness mechanism that allows data distribution between different racks based on the configuration.

Are there any special requirements for namenode?

Yes, the namenode holds information about all files in the system and needs to be extra reliable. The namenode is a single point of failure. It needs to be extra reliable and metadata need to be replicated in multiple places. Note that the community is working on solving the single point of failure issue with the namenode.

If you have a file 128M size and replication factor is set to 3, how many blocks can you find on the cluster that will correspond to that file (assuming the default apache and cloudera configuration)?

6.Based on the configuration settings the file will be divided into multiple blocks according to the default block size of 64M. 128M / 64M = 2 . Each block will be replicated according to replication factor settings (default 3). 2 * 3 = 6 .

What is distributed copy (distcp)?

Distcp is a Hadoop utility for launching MapReduce jobs to copy data. The primary usage is for copying a large amount of data. One of the major challenges in the Hadoop enviroment is copying data across multiple clusters and distcp will allow multiple datanodes to be leveraged for parallel copying of the data.

What is replication factor?

Replication factor controls how many times each individual block can be replicated –
Data is replicated in the Hadoop cluster based on the replication factor. The high replication factor guarantees data availability in the event of failure.

What daemons run on Master nodes?

NameNode, Secondary NameNode and JobTracker
Hadoop is comprised of five separate daemons and each of these daemon run in its own JVM. NameNode, Secondary NameNode and JobTracker run on Master nodes. DataNode and TaskTracker run on each Slave nodes.

What is rack awareness?

Rack awareness is the way in which the namenode decides how to place blocks based on the rack definitions. Hadoop will try to minimize the network traffic between datanodes within the same rack and will only contact remote racks if it has to. The namenode is able to control this due to rack awareness

What is the role of the jobtracker in an Hadoop cluster? 

The jobtracker is responsible for scheduling tasks on slave nodes, collecting results, retrying failed tasks. The job tracker is the main component of the map-reduce execution. It control the division of the job into smaller tasks, submits tasks to individual tasktracker, tracks the progress of the jobs and reports results back to calling code.

How does the Hadoop cluster tolerate datanode failures?

Since Hadoop is design to run on commodity hardware, the datanode failures are expected. Namenode keeps track of all available datanodes and actively maintains replication factor on all data.

The namenode actively tracks the status of all datanodes and acts immediately if the datanodes become non-responsive. The namenode is the central "brain" of the HDFS and starts replication of the data the moment a disconnect is detected.

What is the procedure for namenode recovery?

A namenode can be recovered in two ways: starting new namenode from backup metadata or promoting secondary namenode to primary namenode. 

The namenode recovery procedure is very important to ensure the reliability of the data.It can be accomplished by starting a new namenode using backup data or by promoting the secondary namenode to primary.

Web-UI shows that half of the datanodes are in decommissioning mode. What does that mean? Is it safe to remove those nodes from the network?

This means that namenode is trying retrieve data from those datanodes by moving replicas to remaining datanodes. There is a possibility that data can be lost if administrator removes those datanodes before decomissioning finished . 

Due to replication strategy it is possible to lose some data due to datanodes removal en masse prior to completing the decommissioning process. Decommissioning refers to namenode trying to retrieve data from datanodes by moving replicas to remaining datanodes.

What does the Hadoop administrator have to do after adding new datanodes to the Hadoop cluster?

Since the new nodes will not have any data on them, the administrator needs to start the balancer to redistribute data evenly between all nodes.
Hadoop cluster will detect new datanodes automatically. However, in order to optimize the cluster performance it is recommended to start rebalancer to redistribute the data between datanodes evenly.

If the Hadoop administrator needs to make a change, which configuration file does he need to change?

Each node in the Hadoop cluster has its own configuration files and the changes needs to be made in every file. One of the reasons for this is that configuration can be different for every node.

Map Reduce jobs are failing on a cluster that was just restarted. They worked before restart. What could be wrong?

The cluster is in a safe mode. The administrator needs to wait for namenode to exit the safe mode before restarting the jobs again 

This is a very common mistake by Hadoop administrators when there is no secondary namenode on the cluster and the cluster has not been restarted in a long time. The namenode will go into safemode and combine the edit log and current file system timestamp

Map Reduce jobs take too long. What can be done to improve the performance of the cluster?

One the most common reasons for performance problems on Hadoop cluster is uneven distribution of the tasks. The number tasks has to match the number of available slots on the cluster
Hadoop is not a hardware aware system. It is the responsibility of the developers and the administrators to make sure that the resource supply and demand match.

How often do you need to reformat the namenode?

Never. The namenode needs to formatted only once in the beginning. Reformatting of the namenode will lead to lost of the data on entire 

The namenode is the only system that needs to be formatted only once. It will create the directory structure for file system metadata and create namespaceID for the entire file system.

After increasing the replication level, I still see that data is under replicated. What could be wrong?

Data replication takes time due to large quantities of data. The Hadoop administrator should allow sufficient time for data replication
Depending on the data size the data replication will take some time. Hadoop cluster still needs to copy data around and if data size is big enough it is not uncommon that replication will take from a few minutes to a few hours.

The 36 best tools for data visualization

Creating charts and infographics can be time-consuming. But these tools make it easier.

It's often said that data is the new world currency, and the web is the exchange bureau through which it's traded. As consumers, we're positively swimming in data; it's everywhere from labels on food packaging design to World Health Organisation reports. As a result, for the designer it's becoming increasingly difficult to present data in a way that stands out from the mass of competing data streams.

One of the best ways to get your message across is to use a visualization to quickly draw attention to the key messages, and by presenting data visually it's also possible to uncover surprising patterns and observations that wouldn't be apparent from looking at stats alone.

Not a web designer or developer? You may prefer Free tools for creating infographics.
As author, data journalist and information designer David McCandless said in his TED talk: "By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you're lost in information, an information map is kind of useful."

There are many different ways of telling a story, but everything starts with an idea. So to help you get started we've rounded up some of the most awesome data visualization tools available on the web.

01. Dygraphs:

Help visitors explore dense data sets with JavaScript library Dygraphs

Dygraphs is a fast, flexible open source JavaScript charting library that allows users to explore and interpret dense data sets. It's highly customizable, works in all major browsers, and you can even pinch to zoom on mobile/tablet devices.

02. ZingChart:

ZingChart lets you create HTML5 Canvas charts and more

ZingChart is a JavaScript charting library and feature-rich API set that lets you build interactive Flash or HTML5 charts. It offer over 100 chart types to fit your data.

03. InstantAtlas:

InstantAtlas enables you to create highly engaging visualisations around map data

If you're looking for a data viz tool with mapping, InstantAtlas is worth checking out. This tool enables you to create highly-interactive dynamic and profile reports that combine statistics and map data to create engaging data visualizations.

04. Timeline:

Timeline creates beautiful interactive visualizations

Timeline is a fantastic widget which renders a beautiful interactive timeline that responds to the user's mouse, making it easy to create advanced timelines that convey a lot of information in a compressed space. Each element can be clicked to reveal more in-depth information, making this a great way to give a big-picture view while still providing full detail.

05. Exhibit:

Exhibit makes data visualization a doddle

Developed by MIT, and fully open-source, Exhibit makes it easy to create interactive maps, and other data-based visualizations that are orientated towards teaching or static/historical based data sets, such as flags pinned to countries, or birth-places of famous people.

06. Modest Maps:

Integrate and develop interactive maps within your site with this cool tool

Modest Maps is a lightweight, simple mapping tool for web designers that makes it easy to integrate and develop interactive maps within your site, using them as a data visualization tool.

The API is easy to get to grips with, and offers a useful number of hooks for adding your own interaction code, making it a good choice for designers looking to fully customise their user's experience to match their website or web app. The basic library can also be extended with additional plugins, adding to its core functionality and offering some very useful data integration options.

07. Leaflet:

Use OpenStreetMap data and integrate data visualisation in an HTML5/CSS3 wrapper

Another mapping tool, Leaflet makes it easy to use OpenStreetMap data and integrate fully interactive data visualisation in an HTML5/CSS3 wrapper. The core library itself is very small, but there are a wide range of plugins available that extend the functionality with specialist functionality such as animated markers, masks and heatmaps. Perfect for any project where you need to show data overlaid on a geographical projection.

08. WolframAlpha:

Wolfram Alpha is excellent at creating charts

Billed as a "computational knowledge engine", the Google rival WolframAlpha is really good at intelligently displaying charts in response to data queries without the need for any configuration. If you're using publically available data, this offers a simple widget builder to make it really simple to get visualizations on your site.

09. Visual.ly:

Visual.ly makes data visualization as simple as it can be

Visual.ly is a combined gallery and infographic generation tool. It offers a simple toolset for building stunning data representations, as well as a platform to share your creations. This goes beyond pure data visualisation, but if you want to create something that stands on its own, it's a fantastic resource and an info-junkie's dream come true!

10.Visualize Free:

Make visualizations for free!
Visualize Free is a hosted tool that allows you to use publicly available datasets, or upload your own, and build interactive visualizations to illustrate the data. The visualizations go well beyond simple charts, and the service is completely free plus while development work requires Flash, output can be done through HTML5.

11. Better World Flux:

Making the ugly beautiful - that's Better World Flux
Orientated towards making positive change to the world, Better World Flux has some lovely visualizations of some pretty depressing data. It would be very useful, for example, if you were writing an article about world poverty, child undernourishment or access to clean water. This tool doesn't allow you to upload your own data, but does offer a rich interactive output.

12. jQuery Visualize:

jQuery Visualize Plugin is an open source charting plugin
Written by the team behind jQuery's ThemeRoller and jQuery UI websites, jQuery Visualize Plugin is an open source charting plugin for jQuery that uses HTML Canvas to draw a number of different chart types. One of the key features of this plugin is its focus on achieving ARIA support, making it friendly to screen-readers. It's free to download from this page on GitHub.

13. jqPlot:

jqPlot is a nice solution for line and point charts
Another jQuery plugin, jqPlot is a nice solution for line and point charts. It comes with a few nice additional features such as the ability to generate trend lines automatically, and interactive points that can be adjusted by the website visitor, updating the dataset accordingly.

14. Dipity:

Dipity has free and premium versions to suit your needs

Dipity allows you to create rich interactive timelines and embed them on your website. It offers a free version and a premium product, with the usual restrictions and limitations present. The timelines it outputs are beautiful and fully customisable, and are very easy to embed directly into your page.

15. Many Eyes:

Many Eyes was developed by IBM
Developed by IBM, Many Eyes allows you to quickly build visualizations from publically available or uploaded data sets, and features a wide range of analysis types including the ability to scan text for keyword density and saturation. This is another great example of a big company supporting research and sharing the results openly.

16. D3.js:

You can render some amazing diagrams with D3
D3.js is a JavaScript library that uses HTML, SVG, and CSS to render some amazing diagrams and charts from a variety of data sources. This library, more than most, is capable of some seriously advanced visualizations with complex data sets. It's open source, and uses web standards so is very accessible. It also includes some fantastic user interaction support.

17. JavaScript InfoVis Toolkit:

JavaScript InfoVis Toolkit includes a handy modular structure
A fantastic library written by Nicolas Belmonte, the JavaScript InfoVis Toolkit includes a modular structure, allowing you to only force visitors to download what's absolutely necessary to display your chosen data visualizations. This library has a number of unique styles and swish animation effects, and is free to use (although donations are encouraged).

18. jpGraph:

jpGraph is a PHP-based data visualization tool
If you need to generate charts and graphs server-side, jpGraph offers a PHP-based solution with a wide range of chart types. It's free for non-commercial use, and features extensive documentation. By rendering on the server, this is guaranteed to provide a consistent visual output, albeit at the expense of interactivity and accessibility.

19. Highcharts:

Highcharts has a huge range of options available
Highcharts is a JavaScript charting library with a huge range of chart options available. The output is rendered using SVG in modern browsers and VML in Internet Explorer. The charts are beautifully animated into view automatically, and the framework also supports live data streams. It's free to download and use non-commercially (and licensable for commercial use). You can also play with the extensive demos using JSFiddle.

20. Google Charts:

Google Charts has an excellent selection of tools available
The seminal charting solution for much of the web, Google Charts is highly flexible and has an excellent set of developer tools behind it. It's an especially useful tool for specialist visualizations such as geocharts and gauges, and it also includes built-in animation and user interaction controls.

21. Excel:

It isn't graphically flexible, but Excel is a good way to explore data: for example, by creating 'heat maps' like this one

You can actually do some pretty complex things with Excel, from 'heat maps' of cells to scatter plots. As an entry-level tool, it can be a good way of quickly exploring data, or creating visualizations for internal use, but the limited default set of colours, lines and styles make it difficult to create graphics that would be usable in a professional publication or website. Nevertheless, as a means of rapidly communicating ideas, Excel should be part of your toolbox

.
Excel comes as part of the commercial Microsoft Office suite, so if you don't have access to it, Google's spreadsheets - part ofGoogle Docs and Google Drive - can do many of the same things. Google 'eats its own dog food', so the spreadsheet can generate the same charts as the Google Chart API. This will get your familiar with what is possible before stepping off and using the API directly for your own projects.

22. CSV/JSON:

CSV (Comma-Separated Values) and JSON (JavaScript Object Notation) aren't actual visualization tools, but they are common formats for data. You'll need to understand their structures and how to get data in or out of them.

23. Crossfilter:

Crossfilter in action: by restricting the input range on any one chart, data is affected everywhere. This is a great tool for dashboards or other interactive tools with large volumes of data behind them

As we build more complex tools to enable clients to wade through their data, we are starting to create graphs and charts that double as interactive GUI widgets. JavaScript library Crossfilter can be both of these. It displays data, but at the same time, you can restrict the range of that data and see other linked charts react.

24. Tangle:

Tangle creates complex interactive graphics. Pulling on any one of the knobs affects data throughout all of the linked charts. This creates a real-time feedback loop, enabling you to understand complex equations in a more intuitive way
The line between content and control blurs even further with Tangle. 

When you are trying to describe a complex interaction or equation, letting the reader tweak the input values and see the outcome for themselves provides both a sense of control and a powerful way to explore data. JavaScript library Tangle is a set of tools to do just this. Dragging on variables enables you to increase or decrease their values and see an accompanying chart update automatically. The results are only just short of magical.

25. Polymaps:

Aimed more at specialist data visualisers, the Polymaps library creates image and vector-tiled maps using SVG
Polymaps is a mapping library that is aimed squarely at a data visualization audience. Offering a unique approach to styling the the maps it creates, analagous to CSS selectors, it's a great resource to know about.

26. OpenLayers:

It isn't easy to master, but OpenLayers is arguably the most complete, robust mapping solution discussed here
OpenLayers is probably the most robust of these mapping libraries. The documentation isn't great and the learning curve is steep, but for certain tasks nothing else can compete. When you need a very specific tool no other library provides, OpenLayers is always there.

27. Kartograph:

Kartograph's projections breathe new life into our standard slippy maps

Kartograph's tag line is 'rethink mapping' and that is exactly what its developers are doing. We're all used to the Mercator projection, but Kartograph brings far more choices to the table. If you aren't working with worldwide data, and can place your map in a defined box, Kartograph has the options you need to stand out from the crowd.

28. CartoDB:

CartoDB provides an unparalleled way to combine maps and tabular data to create visualisations
CartoDB is a must-know site. The ease with which you can combine tabular data with maps is second to none. For example, you can feed in a CSV file of address strings and it will convert them to latitudes and longitudes and plot them on a map, but there are many other users. It's free for up to five tables; after that, there are monthly pricing plans.

29. Processing:

Processing provides a cross-platform environment for creating images, animations, and interactions
Processing has become the poster child for interactive visualizations. It enables you to write much simpler code which is in turn compiled into Java. There is also a Processing.jsproject to make it easier for websites to use Processing without Java applets, plus a port to Objective-C so you can use it on iOS. It is a desktop application, but can be run on all platforms, and given that it is now several years old, there are plenty of examples and code from the community.

30. NodeBox:

NodeBox is a quick, easy way for Python-savvy developers to create 2D visualisations
NodeBox is an OS X application for creating 2D graphics and visualizations. You need to know and understand Python code, but beyond that it's a quick and easy way to tweak variables and see results instantly. It's similar to Processing, but without all the interactivity.

31. R:

A powerful free software environment for statistical computing and graphics, R is the most complex of the tools listed here
How many other pieces of software have an entire search enginededicated to them? A statistical package used to parse large data sets, R is a very complex tool, and one that takes a while to understand, but has a strong community and package library, with more and more being produced. The learning curve is one of the steepest of any of these tools listed here, but you must be comfortable using it if you want to get to this level.

32. Weka:

A collection of machine-learning algorithms for data-mining tasks, Weka is a powerful way to explore data
When you get deeper into being a data scientist, you will need to expand your capabilities from just creating visualizations to data mining. Weka is a good tool for classifying and clustering data based on various attributes - both powerful ways to explore data - but it also has the ability to generate simple plots.

33. Gephi:

Gephi in action. Coloured regions represent clusters of data that the system is guessing are similar
When people talk about relatedness, social graphs and co-relations, they are really talking about how two nodes are related to one another relative to the other nodes in a network. The nodes in question could be people in a company, words in a document or passes in a football game, but the maths is the same. Gephi, a graph-based visualiser and data explorer, can not only crunch large data sets and produce beautiful visualizations, but also allows you to clean and sort the data. It's a very niche use case and a complex piece of software, but it puts you ahead of anyone else in the field who doesn't know about this gem.

34. iCharts:

iCharts can have interactive elements, and you can pull in data from Google Docs
The iCharts service provides a hosted solution for creating and presenting compelling charts for inclusion on your website. There are many different chart types available, and each is fully customisable to suit the subject matter and colour scheme of your site.
Charts can have interactive elements, and can pull data from Google Docs, Excel spreadsheets and other sources. The free account lets you create basic charts, while you can pay to upgrade for additional features and branding-free options.

35. Flot:

Create animated visualisations with this jQuery plugin
Flot is a specialised plotting library for jQuery, but it has many handy features and crucially works across all common browsers including Internet Explorer 6. Data can be animated and, because it's a jQuery plugin, you can fully control all the aspects of animation, presentation and user interaction. This does mean that you need to be familiar with (and comfortable with) jQuery, but if that's the case, this makes a great option for including interactive charts on your website.

36. Raphaël

This handy JavaScript library offers a range of data visualisation options

This handy JavaScript library offers a wide range of data visualization options which are rendered using SVG. This makes for a flexible approach that can easily be integrated within your own web site/app code, and is limited only by your own imagination. That said, it's a bit more hands-on than some of the other tools featured here (a victim of being so flexible), so unless you're a hardcore coder, you might want to check out some of the more point-and-click orientated options first!

Further reading

A great Tumblr blog for visualization examples and inspiration:vizualize.tumblr.comNicholas Felton's annual reports are now infamous, but he also has a Tumblr blog of great things he finds.

From the guy who helped bring Processing into the world:benfry.com/writingStamen Design is always creating interesting projects:stamen.com

Eyeo Festival brings some of the greatest minds in data visualization together in one place, and you can watch the videos online.