Big data analysis is the buzzword in technology that has gained massive traction in recent time. Companies now have a huge volume of data which is increasing at an exponential rate. Due to the increased number of customer touchpoints from digital communications like social media and emergence of new innovative technologies like IoT, companies now have data sources that give real-time information. Unfortunately, data alone is nothing but a fad. To make sense out of this data you need sophisticated big data tools and skilled data scientists. Once you satisfy these two prerequisites you will be ready to generate valuable insights from big data. The choice of tools here is an important consideration because once you start a project, it is very complicated and resource intensive to migrate from one solution to another. Here we will go through some popular big data tools to help you make the right decision.

Looking for a data scientist? Know about the essential skills you need to look for.

Top 5 Open-source Big Data Tools:

In this blog, we will analyze the 5 prominent big data tools and how they can be used to make sense of the voracious amount of data:

1. Hadoop:

Hadoop is the most popular big data tool used for analyzing large volumes of data. It was created in 2006 by computer scientists Doug Cutting and Mike Cafarella. By using a distributed cloud storage model this open source, Java-based programming framework enables the processing and storage of extremely large datasets.

With Hadoop, you can run applications on systems with thousands of commodity hardware nodes which makes it perfect for handling big data. The Hadoop Distributed File System (HDFS) facilitates rapid data transfer among nodes and allows the system to continue operations even in case of a node failure. Hadoop capabilities to make multiple copies of data over a distributed storage prevents unexpected data loss even if a sizeable number of nodes become inoperative. Hadoop is now the foundation for many big data processing tasks, like statistical analytics, business, and sales planning, and processing enormous volumes of sensor data, including from internet of things (IoT) sensors.

Read More: Flume vs. Kafka vs. Kinesis – A detailed guide on Hadoop ingestion tools

2. HPCC:

HPCC (High-Performance Computing Cluster), is an open source, big data computing platform developed by LexisNexis Risk Solutions. The public release of HPCC was announced in 2011.

The HPCC platform combines a range of big data analysis tools. It is a package solution with tools for data profiling, cleansing, job scheduling and automation. Like Hadoop, it also leverages commodity computing clusters to provide high-performance, parallel data processing for big data applications.

It uses ECL (a language specially designed to work with big data) as the scripting language for ETL engine. The HPCC platform supports both parallel batch data processing (Thor) and real-time query applications using indexed data files (Roxie).

3. Cassandra:

Apache Cassandra is a distributed NoSQL database for managing copious amounts of structured data across many commodity servers. It not only provides highly available service but also manages nodes efficiently leaving no single point of failure. With capabilities like continuous availability, linear scale performance, operational simplicity, and easy data distribution Apache Cassandra provides a solution which is unmatchable by relational databases.

Apache Cassandra’s architecture accounts for its ability to scale, perform, and offer continuous uptime. Apache Cassandra offers a masterless “ring” design that is intuitive and user-friendly. This beats down other legacy master-slave or complex manual shared architecture. In Apache Cassandra, all nodes play an identical role, communicating with each other equally. This means that it has no single point of failure thereby offering true continuous availability and uptime. You can easily add new nodes to an existing cluster without having to take it down.

4. Apache SAMOA:

SAMOA stand for Scalable Advanced Massive Online Analysis. It is an open source platform build for mining big data streams with a special emphasis on machine learning enablement. SAMOA supports Write-Once-Run-Anywhere (WORA) architecture which allows for seamless integration of multiple Distributed Stream Processing Engines (DSPEs) into the framework. Apache SAMOA allows for the development of new ML algorithms while avoiding the complexities of directly dealing with the distributed stream processing engines (DSPEs, such as Apache Storm, Apache Flink, and Apache Samza).

5. Elasticsearch:

Elasticsearch is a dependable and safe open source platform where you can take any data from any source, in any format and search, analyze it and envision it in real time. Elasticsearch is designed for horizontal scalability, reliability, and ease of management. All of this achieved while combining the speed of search with the potential of analytics. It is based on Lucene a retrieval software library originally compiled in Java. It uses a developer-friendly, JSON-style, query language that works well for structured, unstructured and time-series data.

Read More: Elasticsearch vs Hadoop MapReduce for Analytics

If you are looking for a big data solution partner then you are in the right place. With a vivid experience of working on many big data tools and solutions, we have developed deep expertise in this domain. Contact us today for a project or consultation.