So continuing the old post, here we will discuss some more components of Hadoop ecosystem.
Data Integration or ETL Components of Hadoop Ecosystem
Sqoop (SQL-to-Hadoop) is a big data tool that offers the capability to extract bulk data from non-Hadoop or relational databases (like MySQL, Oracle,Teradata, Postgre) , transform the data into a form usable by Hadoop, and then load the data into HDFS, Hbase or Hive also. This process is similar to Extract, Transform, and Load.It parallelizes data transfer for fast performance, copies data quickly from external system to Hadoop & makes data analysis more efficient.
It’s batch oriented and not suitable for low latency interactive queries. It provides a scalable processing environment for both structured and non-structured data.
The import tool imports individual tables from RDBMS to HDFS. Each row in a table is treated as a record in HDFS. All records are stored as text data in text files or as binary data in Avro and Sequence files.
The export tool exports a set of files from HDFS back to an RDBMS. The files given as input to Sqoop contain records, which are called as rows in table. Those are read and parsed into a set of records and delimited with user-specified delimiter.
Sqoop Use Case-
Coupons.com , Apollo Group uses Sqoop component of the Hadoop ecosystem to enable transmission of data between Hadoop & data warehouse .
Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming or log files data into the Hadoop Distributed File System (HDFS). It is used for collecting data from its origin and sending it back to the resting location (HDFS).Flume accomplishes this by outlining data flows that consist of 3 primary structures channels, sources and sinks. The processes that run the dataflow with flume are known as agents and the bits of data that flow via flume are known as events.
Flume helps to collect data from a variety of sources, like logs, jms, Directory etc.
Multiple flume agents can be configured to collect high volume of data.
It scales horizontally & is stream oriented.It provides high throughput and low latency.It is fault tolerant.
Both Sqoop and Flume, pull the data from the source and push it to the sink. The main difference is Flume is event driven, while Sqoop is not.
Flume Use Case –
Twitter source connects through the streaming API and continuously downloads the tweets (called as events). These tweets are converted into JSON format and sent to the downstream Flume sinks for further analysis of tweets and retweets to engage users on Twitter.
Goibibo uses Flume to transfer logs from production system to HDFS.
Data Storage Component of Hadoop Ecosystem
Hbase is an open source, distributed, sorted map model.Its a column store-based NoSQL database solution & is similar to Google’s BigTable framework.It supports random reads and also batch computations using MapReduce. With HBase NoSQL database enterprise can create large tables with millions of rows and columns on hardware machine. The best practice to use HBase is when there is a requirement for random ‘read or write’ access to big datasets. HBase’s important advantage is that it supports updates on larger tables and faster lookup. The HBase data store supports linear and modular scaling. HBase stores data as a multidimensional map and is distributed. HBase operations are all MapReduce tasks that run in a parallel manner.
Its well integrated with Pig/Hive/Sqoop. It is consistent and partition tolerant system in CAP theorem.
HBase Use Case-
Facebook is one the largest users of HBase with its messaging platform built on top of HBase in 2010.
Apache Cassandra is a free and open-source distributed database management system designed to handle large amounts of data across many commodity servers.This database is the right choice when you need scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform for mission-critical data.Cassandra’s support for replicating across multiple data-centers is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.
For Cassandra, Twitter is an excellent example. We know that, like most sites, user information (screen name, password, email address, etc), is kept for everyone and that those entries are linked to one another to map friends and followers. And, it wouldn’t be Twitter if it weren’t storing tweets, which in addition to the 140 characters of text are also associated with meta-data like timestamp and the unique id that we see in the URLs.
Monitoring, Management and Orchestration Components of Hadoop Ecosystem- Oozie and Zookeeper
Oozie is a workflow scheduler where the workflows are expressed as Directed Acyclic Graphs. Oozie runs in a Java servlet container Tomcat and makes use of a database to store all the running workflow instances, their states ad variables along with the workflow definitions to manage Hadoop jobs (MapReduce, Sqoop, Pig and Hive).The workflows in Oozie are executed based on data and time dependencies.
Oozie Use Case:
The American video game publisher Riot Games uses Hadoop and the open source tool Oozie to understand the player experience.
Zookeeper is the king of coordination and provides simple, fast, reliable and ordered operational services for a Hadoop cluster. Zookeeper is responsible for synchronization service, distributed configuration service and for providing a naming registry for distributed systems.
Zookeeper Use Case-
Found by Elastic uses Zookeeper comprehensively for resource allocation, leader election, high priority notifications and discovery. The entire service of Found built up of various systems that read and write to Zookeeper.
Here is the recorded session from the IBM Certified Hadoop Developer Course at DeZyre about the components of Hadoop Ecosystem –
Several other common Hadoop ecosystem components include: Avro, Cassandra, Chukwa, Mahout, HCatalog, Ambari and Hama. By implementing Hadoop using one or more of the Hadoop ecosystem components, users can personalize their big data experience to meet the changing business requirements. The demand for big data analytics will make the elephant stay in the big data room for quite some time.
Data Serialisation (Data Interchange Protocols)
AVRO: Apache Avro is a language-neutral data serialization system, developed by Apache Hadoop.Data serialization is a mechanism to translate data in computer environment (like memory buffer, data structures or object state) into binary or textual form that can be transported over network or stored in some persistent storage media.Java and Hadoop provides serialization APIs, which are java based, but Avro is not only language independent but also it is schema-based.Once the data is transported over network or retrieved from the persistent storage, it needs to be deserialized again. Serialization is termed as marshalling and deserialization is termed as unmarshalling.
Avro uses JSON format to declare the data structures. Presently, it supports languages such as Java, C, C++, C#, Python, and Ruby.Avro has a schema-based system. A language-independent schema is associated with its read and write operations.
Like Avro, there are other serialization mechanisms in Hadoop such as Sequence Files, Protocol Buffers, and Thrift.Avro creates a self-describing file named Avro Data File, in which it stores data along with its schema in the metadata section.Avro is also used in Remote Procedure Calls (RPCs). During RPC, client and server exchange schemas in the connection handshake.
To serialize Hadoop data, there are two ways −
- You can use the Writable classes, provided by Hadoop’s native library.
- You can also use Sequence Files which store the data in binary format.
The main drawback of these two mechanisms is that Writables and SequenceFiles have only a Java API and they cannot be written or read in any other language.
Therefore any of the files created in Hadoop with above two mechanisms cannot be read by any other third language, which makes Hadoop as a limited box. To address this drawback, Doug Cutting created Avro, which is a language independent data structure.
Avro provides rich data structures. For example, you can create a record that contains an array, an enumerated type, and a sub record. These datatypes can be created in any language, can be processed in Hadoop, and the results can be fed to a third language.
Thrift is a lightweight, language-independent software stack with an associated code generation mechanism for RPC. Thrift provides clean abstractions for data transport, data serialization, and application level processing. Thrift was originally developed by Facebook and now it is open sourced as an Apache project. Apache Thrift is a set of code-generation tools that allows developers to build RPC clients and servers by just defining the data types and service interfaces in a simple definition file. Given this file as an input, code is generated to build RPC clients and servers that communicate seamlessly across programming languages.
Thrift supports a variety of languages including C++, Java, Python, PHP, Ruby.
To learn more on Hadoop…keep on reading these tutorials…every day we try to get something new and interesting for all my readers !!
Now we will start learning all Ecosystem components in more detail. Click here to read about how MapReduce Algorithm works with an easy example.