Big Data analytics is an essential part of any business workflow nowadays. To make the most of it, we recommend using these popular open source Big Data solutions for each stage of data processing.

Why opting for open source Big Data tools and not for proprietary solutions, you might ask? The reason became obvious over the last decade — open sourcing the software is the way to make it popular.

Developers prefer to avoid vendor lock-in and tend to use free tools for the sake of versatility, as well as due to the possibility to contribute to the evolvement of their beloved platform. Open source products boast the same, if not better level of documentation depth, along with a much more dedicated support from the community, who are also the product developers and Big Data practitioners, who know what they need from a product. Thus said, this is the list of 8 hot Big Data tool to use in 2018, based on popularity, feature richness and usefulness.

1. Apache Hadoop

The long-standing champion in the field of Big Data processing, well-known for its capabilities for huge-scale data processing. This open source Big Data framework can run on-prem or in the cloud and has quite low hardware requirements. The main Hadoop benefits and features are as follows:

HDFS — Hadoop Distributed File System, oriented at working with huge-scale bandwidth

MapReduce — a highly configurable model for Big Data processing

YARN — a resource scheduler for Hadoop resource management

Hadoop Libraries — the needed glue for enabling third party modules to work with Hadoop

2. Apache Spark

Apache Spark is the alternative — and in many aspects the successor — of Apache Hadoop. Spark was built to address the shortcomings of Hadoop and it does this incredibly well. For example, it can process both batch data and real-time data, and operates 100 times faster than MapReduce. Spark provides the in-memory data processing capabilities, which is way faster than disk processing leveraged by MapReduce. In addition, Spark works with HDFS, OpenStack and Apache Cassandra, both in the cloud and on-prem, adding another layer of versatility to big data operations for your business.

3. Apache Storm

Storm is another Apache product, a real-time framework for data stream processing, which supports any programming language. Storm scheduler balances the workload between multiple nodes based on topology configuration and works well with Hadoop HDFS. Apache Storm has the following benefits:

Great horizontal scalability

Built-in fault-tolerance

Auto-restart on crashes

Clojure-written

Works with Direct Acyclic Graph(DAG) topology

Output files are in JSON format

4. Apache Cassandra

Apache Cassandra is one of the pillars behind Facebook’s massive success, as it allows to process structured data sets distributed across huge number of nodes across the globe. It works well under heavy workloads due to its architecture without single points of failure and boasts unique capabilities no other NoSQL or relational DB has, such as:

Great liner scalability

Simplicity of operations due to a simple query language used

Constant replication across nodes

Simple adding and removal of nodes from a running cluster

High fault tolerance

Built-in high-availability

5. MongoDB

MongoDB is another great example of an open source NoSQL database with rich features, which is cross-platform compatible with many programming languages. IT Svit uses MongoDB in a variety of cloud computing and monitoring solutions, and we specifically developed a module for automated MongoDB backups using Terraform. The most prominent MongoDB features are:

Stores any type of data, from text and integer to strings, arrays, dates and boolean

Cloud-native deployment and great flexibility of configuration

Data partitioning across multiple nodes and data centers

Significant cost savings, as dynamic schemas enable data processing on the go

6. R Programming Environment

R is mostly used along with JuPyteR stack (Julia, Python, R) for enabling wide-scale statistical analysis and data visualization. JupyteR Notebook is one of 4 most popular Big Data visualization tools, as it allows composing literally any analytical model from more than 9,000 CRAN (Comprehensive R Archive Network) algorithms and modules, running it in a convenient environment, adjusting it on the go and inspecting the analysis results at once. The main benefits of using R are as follows:

R can run inside the SQL server

R runs on both Windows and Linux servers

R supports Apache Hadoop and Spark

R is highly portable

R easily scales from a single test machine to vast Hadoop data lakes

7. Neo4j

Neo4j is an open source graph database with interconnected node-relationship of data, which follows the key-value pattern in storing data. IT Svit has recently built a resilient AWS infrastructure with Neo4j for one of our customers and the database performs well under heavy workload of network data and graph-related requests. Main Neo4j features are as follows:

Built-in support for ACID transactions

Cypher graph query language

High-availability and scalability

Flexibility due to the absence of schemas

Integration with other databases

8. Apache SAMOA

This is another of the Apache family of tools used for Big Data processing. Samoa specializes at building distributed streaming algorithms for successful Big Data mining. This tool is built with pluggable architecture and must be used atop other Apache products like Apache Storm we mentioned earlier. Its other features used for Machine Learning include the following:

Clustering

Classification

Normalization

Regression

Programming primitives for building custom algorithms

Using Apache Samoa enables the distributed stream processing engines to provide such tangible benefits:

Program once, use anywhere

Reuse the existing infrastructure for new projects

No reboot or deployment downtime

No need for backups or time-consuming updates