Sahil 
Do you find it challenging to manage the quantity of data that you collect in your organization? Have you been struggling to manage data sets that range from medium to large in terms of difficulty to work through? If so and you’re not alone are two sentences that may come in handy. Today several users are struggling to manage a large amount of information. Thankfully there is Apache Hadoop and the framework that accompanies it, which could assist in solving these problems.
This blog post aims to give an overview of the five most important Big Data Hadoop tools Data professional must know. With these tools, you can go a long way in improving your capacity to manage big data and other forms of data and gain insights into the future growth of your business. At Gyansetu, we have a Big Data Course In Gurgaon that has inculcated these instruments among others.
1. Apache Hadoop HDFS (Hadoop Distributed File Systems)
The Hadoop ecosystem incorporates HDFS and the basic storage system is signed to manage ultra-large datasets across a distributed system. However, what sets HDFS apart from other distributed file systems is still unknown.
HDFS is designed to store and process overly big files usually in the scale of gigabytes to terabytes and spanning across different machines inside a cluster. It partitions these bigger files into chunk sizes which can be either 128 MB or 256 MB, and spreads them across multiple nodes in a cluster. undefined
Scalability: As your data grows, you can easily add more nodes to your cluster to add storage capacity.
Fault tolerance: HDFS replicates data blocks across multiple nodes and ensures data availability if some nodes fail.
High throughput: By distributing data across multiple machines HDFS allows for parallel processing and significantly speeds up data access and analysis.
For businesses dealing with massive amounts of unstructured or semi-structured data HDFS provides a more reliable and scalable storage solution that forms the foundation for other Hadoop tools and applications.
2. Apache Hive
Have you ever wished you could query your big data using familiar SQL-like syntax? That’s exactly what Apache Hive brings to the table. Hive is a data infrastructure built on top of Hadoop that provides an SQL interface for querying and analyzing larger datasets stored in HDFS.
Kеy fеaturеs of Hivе include:
HiveQL: A SQL query language that allows users to write queries similar to traditional SQL statements.
Schema on read: Hive does not enforce schema when data is loaded but rather when it is queried and offers flexibility in data storage.
Integration with other tools: Hive can be easily integrated with business intelligence tools and data visualization platforms.
Hive is useful for data analysts and business users who are familiar with SQL but may not have extensive programming skills. It allows them to work with big data and make the transition to Hadoop much smoother.
3. Apache Spark
While not strictly a Hadoop tool Apache Spark has been an integral part of many Hadoop-based big data solutions. Spark is an open-source and distributed computing system that provides in-memory processing capabilities and making it significantly faster than traditional Map.
What sets Spark apart?
Speed: Spark can be up to 100 times faster than Hadoop MapReduce for certain applications especially those requiring iterative algorithms or interactive data analysis.
Verticality: Spark supports various types of computing including batch processing interactive queries and streaming machine learning and graph processing.
Easy to use APIs: Spark offers high-level APIs in Java, Scala, Python, and R making it accessible to a wider range of developers and data scientists.
Integration with Hadoop: Spark can run on Hadoop clusters and access data stored in HDFS making it a powerful complement to existing Hadoop installations.
For organizations looking to real-time data processing or build machine learning models on big data Spark is an invaluable tool in the Hadoop ecosystem.
4. Apache HBasee
When it comes to storing and accessing larger amounts of structured data in real time Apache HBase shines. HBase is a column-oriented and non-relational database that runs on top of HDFS and provides real-time read/write access to your big data.
Key features of HBase include:
Linear scalability: HBase can handle tables with billions of rows and millions of columns and scale linearly as data grows.
Consistent readings and writings: Unlike NoSQL databases and HBases provides strong consistency for readings & writings.
Automatic sharding: HBase automatically splits and distributes data across the cluster as it grows.
Integration with MapReduction: HBasic tables can be used as input and output for MapReduction jobs allowing for complete data processing.
HBase is useful for scenarios requiring random and real-time rеad/writе access to larger datasets. It’s commonly used in applications like real-time analytics personalization of engines and time series data storage.
5. Apache Kafka
Last but not least we have Apache Kafka a distributed streaming platform that is an essential tool for building real-time data pipelines and streaming applications. While not originally part of the Hadoop ecosystem Kafka is frequently used alongside Hadoop tools to ingest and process larger volumes of real-time data.
What makes Kafka stand out?
High throughput: Kafka can handle millions of messages per second making it suitable for high-volume data streams.
Durability: Kafka persists messages on disk providing a buffer against data loss and allowing for replay of data streams.
Scalability: Kafka can scale out to handle increasing data volumes by adding more brokers to the cluster.
Integration: Kafka integrates with other Hadoop tools like HDFS Spark and HBase and allows for one-to-one data processing pipelines.
Kafka is useful for building real-time streaming data pipelines monitoring operational data and tracking user activity on websites and applications.
Conclusion
Mastering these top 5 Hadoop tools HDFS, Hive, Spark, HBase, and Kafka can significantly boost your big data capabilities. These powerful technologies work to help organizations store process and analyze massive datasets efficiently.
If you are interested in exploring the high-level package of big data and Hadoop or interested in taking professional training. At Gyansetu, we have made arrangements for the big data course in Gurgaon wherein these tools are taught along with other tools. The hands-on approach is also used to give insight into real-life cases while giving you the practice to deal with such cases as witnessed when using big data in today’s business world.
For freshers, our big data course in Gurgaon is designed to help you get started while if you already have the experience we offer the skills required to harness this growing opportunity with maximum efficiency. Gyansetu offers information on various courses and specializations and is waiting for you to take the first steps toward becoming a big data analyst!
