We are witnessing the reign of the data revolution. Data is getting enormous, and its management is becoming highly challenging. Datasets hide volumes of insights beneath, and no business can afford to dump them. So, is there a midway? Fortunately, yes! As data is getting enormous, new and robust tools are shaping up. Many frameworks like Hadoop have emerged to assist the experts in managing tons of data and driving information from them. Again, we have a myriad of Hadoop tools to make data manipulation a cakewalk.
Whether you are a novice or a professional in data science, Hadoop and its suite of tools can help you dramatically. Since many Hadoop tools are available in the market, choosing the best ones can be a Herculean task. However, you can make a quick decision if you are well-familiar with the top Hadoop tools out there. Besides, learning the top 10 Hadoop tools can be an excellent start to pursuing a Hadoop Certification Course.
. So, let’s not delay anymore and dive into knowing the top 10 Hadoop tools.
#1 Hadoop Distributed File System (HDFS)
The Hadoop Distributed File System (abbreviated as HDFS) is an advanced FAT32 and New Type File System version. Unlike the latter two, HDFS has better storage capacity and can save volumes of data. Many popular websites and Software-as-a-Service (SaaS) use HDFS, and their operations have relied on the Hadoop framework since time immemorial.
#2 NoSQL
Earlier, we used to deal with structured data, and there were no concepts of unstructured datasets. Data manipulation and management were simple and sorted when structured datasets were dominant. However, the modern paradigm has unstructured data as the most common and widely-produced set. Today, social media and various sources generate unstructured data, compelling experts to get their hands dirty with random datasets.
Consequently, primitive tools like Structured Query Language (SQL) have become obsolete. Now, it’s the era for NoSQL that helps deal with unstructured data like a pro. NoSQL remains compatible with various databases and frameworks like Hadoop, Oracle Wallet, and Oracle Database.
#3 Apache HIVE
The renowned server hosting utility Apache introduces the HIVE, a data warehousing solution for Hadoop. The Hadoop-HIVE duo has massive popularity, and experts prefer using them to access several benefits in their work. HIVE is a highly reliable tool that allows storing and processing volumes of data effortlessly. Also, it helps in imparting a structure to the datasets and making them fit for traditional queries and operations.
You can query the database using HiveQL, a query language like SQL, and it has a minimal learning curve. Hive offers several storage solutions like HBase, ORC, and RCFile. Besides, Hive encompasses several pre-built packages to manipulate different data types in the blink of an eye.
#4 Apache Spark
Data analytics has a significant role in the modern era. The Hadoop developers understand the concern and offer some of the best tools for data analytics. Among all data analytics tools, Spark has a leading position in the market. Numerous data professionals use Spark frequently and have leveraged the robust tool into popular apps and utilities. Developed by AMPLab at UC Berkeley, Spark is now a product from Apache.
Apache Spark is highly compatible with HDFS and has a hundred times better processing abilities than other analytical frameworks. Spark works by loading all datasets as clusters and querying them as and when needed. These reasons and its open-source nature makes Spark an ideal framework for all types of users.
#5 MapReduce
Another common term in Hadoop is MapReduce. The framework allows developers to process multiple datasets in parallel. Apart from parallel processing, MapReduce allows dealing with terabytes of data and manipulating them in the blink of an eye.
MapReduce works with JobTracker and TaskTracker components. As the name suggests, the JobTracker records all jobs, and the TaskTracker monitors cluster nodes. The JobTracker acts as a Master and assigns jobs. In contrast, the TaskTracker manages the tasks and reschedules them upon failing.
#6 Clouds
Cloud and Big Data go hand-in-hand. The cloud platforms deal with massive datasets, making the functionalities and features lagging. So, cloud platforms are vigorously adopting Hadoop. You can access such potential by using Clouds.
Clouds allow developers and data professionals to set up temporary machines to process datasets and store the results differently. In the end, you can free up the consumed machine without impacting the system’s speed.
#7 Mahout
Blending different cutting-edge technologies is the latest trend. Likewise, the amalgamation of big data and machine learning is widely popular. Hadoop takes such a combination to an all-new level by introducing a bunch of ML libraries termed under Mahout. You can implement Mahout over Apache Hadoop and leverage MapReduce principles to conduct different tasks regarding Big Data.
#8 GIS Tools
Data science and analytics use geographical information like cafes, restaurants, states, and much more, and this information needs to be accurate and precise. So, Hadoop utilizes GIS tools to comprehend such geographical information and use it for various purposes. The Java-driven tool, GIS, converts locations as strings and lets you integrate maps in different applications.
#9 Flumes
Whenever you query the database, your activity gets registered as logs. Earlier, records used to be small files. However, the size of logs increases as the datasets grow bigger. Also, the logs can help you debug the program and analyze the malicious segments in the blink of an eye. So, it would help if you had powerful equipment to analyze the logs and generate insights. Right here, Flumes makes a grand entry and simplifies log processing dramatically.
#10 Avro
Avro is a revolution in representing massive and complex Big Data. It can easily take inputs, produce outputs, and provide you with formatting abilities for all MapReduce jobs.
Indeed, Hadoop has become a game-changing utility for Big Data. Furthermore, the emergence of a variety of relevant tools makes Hadoop more popular among data professionals. Getting yourself well-acquainted with the above tools can boost your career regardless of your experience in the industry. So, what makes you wait? Learn the above Hadoop tools now!
