Introduction to Hadoop Ecosystem
Hadoop Ecosystem consists of a suite of Apache Hadoop software, also called the Hadoop Big Data Tools. These tools include Apache open-source projects fully equipped with an extensive range of customary solutions and tools that can be leveraged to tackle Big Data challenges. Some popular names in this suite include Apache Spark, Apache Pig, MapReduce, and HDFS. These components can collaborate to solve storage, absorption, analysis, and Data Maintenance issues. Here is a brief introduction to these integral components of the Hadoop Ecosystem:
- Apache Pig: Apache Pig is a high-level scripting language that can be used for Query-based processing of data services. Its primary objective is to execute queries for larger datasets within Hadoop. You can then organize the final output in the desired format for future use.
- Apache Spark: Apache Spark is an in-memory Data Processing Engine that can work well for various operations. Apache Spark features Scala, Java, Python, and R programming languages. Apart from this, it also supports Data Streaming, SQL, Machine Learning, and Graph Processing.
- HDFS: Hadoop Distributed File System (HDFS) is one of the biggest Apache projects that lay the foundation for Hadoop’s primary storage system. You can use HDFS to store large files running over the cluster of commodity software. HDFS follows a DataNode and NameNode architecture.
- MapReduce: MapReduce is a programming-based Data Processing Layer of Hadoop that can easily process large unstructured and structured datasets. MapReduce can also simultaneously manage very large data files by dividing the job into a set of sub-jobs.
Why Do you Need Hadoop Big Data Tools?
Data has become an integral part of your workflows in the last decade with a staggering amount of data being produced every day. To tackle the problem of processing and storing the data companies are scouring the market to pave their way in Digital Transformation. This voluminous data is referred to as Big Data, and it includes all the structured and unstructured datasets, which need to be stored, managed, and processed. This is where Hadoop Big Data Tools can come in handy. These tools can help ease your digital transformation journey.
Best Hadoop Big Data Tools
Here are the 5 best Hadoop Big Data Tools that you can leverage to significantly boost growth:
- Apache Impala
- Apache HBase
- Apache Pig
- Apache Mahout
- Apache Spark
Apache Impala is an open-source SQL Engine that has been ideally designed for Hadoop. Apache Impala provides faster processing speed and eliminates the speed-related issue taking place in Apache Hive. The syntax used by Apache Impala is similar to SQL, the ODBC Driver like the Apache Hive, and the user interface. You can easily integrate this with the Hadoop ecosystem for Big Data Analytics purposes.
Here are a few advantages of leveraging Apache Impala:
- Apache Impala is scalable.
- It provides robust security to its users.
- It also offers easy integrations and in-memory data processing.
Apache HBase is a non-relational DBMS that runs on top of HDFS. It stands out since it is scalable, distributed, open-source, column-oriented, among many other useful functionalities. Apache HBase has been patterned after Google’s Bigtable that provides it with identical capabilities on top of HDFS and Hadoop. Apache HBase is primarily used for consistent, real-time read-write operations on big datasets. This helps ensure minimal latency & a higher throughput while executing operations on Big Data datasets.
Here are a few advantages of leveraging Apache HBase:
- Apache HBase can circumvent the cache for real-time queries.
- It offers linear scalability and modularity.
- A Java API can be utilized for client-based data access.
Apache Pig was initially developed by Yahoo to simplify programming because it has the capability of processing an extensive dataset. It can do this because it works on top of Hadoop. Apache Pig can primarily be used for analyzing more massive datasets by representing them as dataflow. You can also leverage Apache Pig to improve the level of abstraction for processing massive datasets. The scripting language used by developers is Pig Latin, which runs on Pig Runtime.
Here are a few advantages of leveraging Apache Pig:
- Apache Pig houses a diverse set of operators and is fairly easy to program.
- Apart from its ability to handle various kinds of data, Apache Pig also offers extensibility to its users.
Mahout finds its roots in the Hindi word Mahavat, which means an elephant rider. Apache Mahout algorithms are run on top of Hadoop and are ideal when implementing Machine Learning algorithms on the Hadoop ecosystem. A noteworthy feature is that Apache Mahout can easily implement Machine Learning algorithms without any integrations with Hadoop.
Here are a few advantages of leveraging Apache Mahout:
- Apache Mahout can be used for analyzing large datasets.
- Apache Mahout is composed of vector and matrix libraries.
Apache Spark is an open-source framework that can be used in fast cluster computing, data analytics, and machine learning. Apache Spark was primarily designed for batch applications, streaming data processing, and interactive queries.
Here are a few advantages of leveraging Apache Spark:
- Apache Spark has in-memory processing.
- Apache Spark is cost-efficient and easy to use.
- Apache Spark offers a high-level library that can be leveraged for streaming.
This blog talked about the best Hadoop Big Data Tools in the marketplace like Apache Pig, Apache Impala, Apache Spark, Apache HBase, etc. It also gave a quick introduction to the Hadoop ecosystem and the importance of Hadoop Big Data tools.
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