Once upon a time, if you wanted to know what was happening on the other side of the world, you had to wait for weeks for information to arrive in letters or telegraphs says Peter DeCaprio. Nowadays, news travel around the world within seconds. This is due to the advancement of technology, especially in big data technologies.
Big Data technologies are powerful tools that allow decision-makers and businesses alike to take advantage of rich amounts of data that used to go unutilized before. These technologies it allows us to store massive amounts of unstructured data so we can analyze them faster than ever before not just with human eyes but also with machines as well.
This article aims at answering some questions related to the latest big data technologies out there so users don’t have to go all over the internet in search of answers.
1. What is Hadoop?
Hadoop was built on the idea of utilizing commodity hardware (cheap hardware) within large distributed networks (the cloud) to solve some of technology’s grand challenges like indexing the web or processing big data at scale.
2. What is MapReduce?
MapReduce is an algorithm used with Big Data technologies, particularly with the Hadoop framework applications explains Peter DeCaprio. It divides the input data, which can be of any size, into smaller blocks and process these blocks in parallel by different nodes of the Hadoop cluster that eventually results in desired output by the end of the processing phase after running MapReduce jobs.
3. How is the information processed in the Hadoop framework?
First, Hadoop splits files into logical blocks of data and spreads them across nodes in clusters. After that, MapReduce jobs are applying to each block where it’s dividing into various tasks. Which are run concurrently by different machines (nodes) of the Hadoop cluster. Once over with processing, results are collecting back to the master node where the MapReduce framework combines all partial result files into one final output for the end-user or client.
4. What is HDFS?
HDFS stands for Hadoop Distributed File System which is a Java-based file system. Designed especially for storing huge volumes of data that can scale up to petabytes(PBs) of data. It provides a fault-tolerant way of storing big data by replicating the data across multiple nodes within the Hadoop cluster says Peter DeCaprio.
Here’s a complete guide to installing Hortonworks Data Platform (HDP) 2.6 on CentOS 6. x
5. What is Apache Mahout?
Apache Mahout provides various implementations for classic machine learning algorithms like Recommendation, Classification, Clustering, and Focused Crawler which makes it easy for developers to make use of these libraries from their applications without requiring any expertise in building and maintaining those models from scratch which usually takes a lot of time and effort to build a prototype model just to test if that works or not which can be quite costly as well.
6. What is Apache Spark?
Apache Spark is an open-source Big Data processing engine originally develops in the AMPLab at UC Berkeley, which was initially design to improve Hadoop performance by leaps and bounds so that users can run their jobs much faster than before. It supports batch processing, stream processing, and micro-batches to speed up workloads. Furthermore, it speeds up everything on the fly when compared with MapReduce. Because it reuses data storage blocks instead of reprocessing them. It also provides APIs in Java, Python, Scala to make use of these features hassle-free for end-user. Without any hassles to build applications from scratch in each language every time!
7. Does Open Source Hadoop means there are no charges involved?
No. Open source Hadoop is free of cost but it’s not bundling with support. For which users need to get commercial help from companies like Cloudera, Hortonworks, IBM, etc. If they want professional support which costs extra along with software license fees.
8. What is Apache Hive?
Apache Hive provides data warehouse infrastructure built on top of Hadoop. For providing SQL interface to process structured big data in the form of tables. Just like we do in the traditional relational database management systems. It uses the MapReduce paradigm to query massive datasets stored within the HDFS file system and warehoused using Apache HBase as a backend storage engine. Peter DeCaprio says it also supports other interfaces like Apache Pig and Apache Spark SQL. It’s mainly in use for ad-hoc querying, running complex analytical queries on unstructured data with lesser effort. Compared to writing MapReduce code manually in Java in order to achieve the same task.
9. What is Apache ZooKeeper?
ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. For example, we can design a distributed application where different components of that system can communicate with each other0. By having a common directory called as ZNode (Z stands for Zoo) which holds all such information required to perform particular tasks at hand. Like tracking nodes(computer systems) joining or leaving clusters dynamically. All these features make it an essential component for the Hadoop ecosystem as well as other big data projects like Cassandra, etc. Which makes it the first choice of centralized service to manage groups and track changes in cluster machines.
Conclusion:
Oozie is a workflow scheduler system to manage Hadoop jobs at scale. It can trigger downstream dependent jobs after completion or failure of the previous job. Which makes it quite useful to schedule complex batch workflows within the Hadoop Ecosystem. So that we need not worry about managing such things from scratch every time. It provides a graphical interface to interact with running workflows and debug them as required by analysis users.
