Hadoop Terminologies: 20 Most Important Hadoop Terms

Data science, big data and Hadoop are all buzz words. These are no longer buzzwords. The need to process and analyze Big data is increasing with the ever-increasing amount of data being generated. Hadoop has been extensively used to process this Big Data all over the world, and it has quickly become the heart of Big-data technology. It has been integrated with many technologies over the years. Hadoop terminologies refer to the wide range of its ecosystem and associated tools, which are constantly expanding.
There is a growing demand for Hadoop professionals all over the world. It doesn’t matter if it’s market demand or personal career advancement, Hadoop is becoming a synonym for “need of the hour.” However, you need to be familiar with specific Hadoop terms as a Hadoop professional.
Hadoop Terminologies: Top 20 Hadoop Terms From Hadoop Glossary
1. Apache Hadoop
This is the most important Hadoop term. Apache Hadoop, an open-source framework written in Java, can process large amounts of unstructured data. Hadoop is known for being scalable, robust, and fault-tolerant. Apache designed Hadoop so that you can scale it from one server to many (hundreds of) machines in your network.
2. Apache Hive
Apache Hive is Hadoop’s data warehouse infrastructure. To manage data summarization, it uses SQL queries called Hive Query Language (HQL). These queries are used internally to map and reduce jobs for processing.
3. Apache Oozie
Apache Oozie, a Java web application responsible for scheduling Hadoop jobs, is available in Java. It is responsible for the data storage and processing layers of the distributed ecosystem. It combines Hadoop jobs with Oozie Workflow administration and Oozie Coordinator jobs.
4. Apache Pig
Apache Pig is an integral part of Hadoop terminologies. It is a data flow platform responsible for Map Reduce jobs execution. It is an extensible, high-level platform that simplifies programming and optimizes execution. Pig scripts can be converted into Map Reduce jobs, which are then executed on HDFS data.
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5. Apache Spark
Apache Spark is an open-source, cluster computing framework. It can handle in-memory data processing, which is useful for distributed clustered computing such as Hadoop. It is therefore faster than Map Reduce. It runs on top Hadoop clusters. Spark doesn’t have a file system and uses the Hadoop datastore (HDFS).
6. Apache Tez
Apache Tez is a framework for creating high-performance batch and data processing applications. YARN from Apache Hadoop coordinates to it to provide the API and developer framework for writing batch workloads.
7. Apache Zookeeper
Apache Zookeeper is an open-source centralized service that allows distributed coordination among large numbers of hosts. Zookeeper’s API and architecture allows for the synchronization of Hadoop clusters. It uses a Client-server architecture to keep the common objects in the environment.
8. Big Data
Without Big Data, Hadoop glossary is incomplete. It contains large datasets that can be as large as PetaBytes (1015 Bytes). This data can be generated by users of social media sites, the stock exchange, e-commerce sites, and others. Hadoop handles this Big data through proper processing, storage and analysis, along with its distribution system.
9. Flume
Apache Flume is an open-source aggregation service. It is responsible for data collection, and data transport from its source to its destination. It acts as an interface between data sources such as web Servers, Twitter and Facebook, Cloud, and other data sources. It also connects to the Data stores such as HBase or HDFS. It is highly configurable and reliable.
10. Hadoop Common
It is the Hadoop common library that contains jars of common utilities that support the code of other modules within the Hadoop environment. These libraries and jars contain the Java scripts and files required to use Hadoop.
11. HBase
Apache HBase, a column-oriented Hadoop database that stores large amounts of data in scalable ways, is Apache HBase. It is an open-source data model that allows random access to large volumes of data. It is similar in design to Google’s Big table and is built on top HDFS.
12. HCatalog
HCatalog, a Hadoop layer, manages data storage in tables. It allows users to easily write data using MapReduce, Pig, and other Hadoop tools. It also links Hive to Hadoop applications. It makes it easy for users to share data between different tools by using its analytics bench.
13. HDFS
Hadoop Distributed File System provides Hadoop with a layer of storage. It is a distributed filesystem that manages data storage in a distributed fashion. This architecture system has the master.