Data has become a powerful tool for the global workforce. It’s a prerequisite to translate massive amounts of unstructured and structured information into meaningful and valuable business insights for future growth. Hence, the current global market is flooded with a wide range of big data tools to process and store information. Data is meaningless until it is turned into a piece of useful information and knowledge that can aid in the management of an enterprise. The innovation around big data offers a pool of endless functionalities that deals with insight and forecasting to save, efficiency, and minimize cost for an organization. According to a study conducted by International Data Corporation, it is estimated that the global data creation will surpass 163 Zettabytes by the year 2025. This will be a tenfold amount of data that is generated by 2017. Many business enterprises across the globe heavily rely on open-source database solutions to manage their data. Many organizations primarily refer to opt for free tools due to their versatility and the chance to contribute to the platform’s evolution. The world is changing faster, and there is a need for an organization to invest heavily in data analytics. The rapid growth of information and technology developments has provided a platform for enterprises worldwide to develop new database models using large scale analytics. Artificial Intelligence is at the center of major innovation across the world. In this article, I will highlight the Top Ten Open-Source Big Data Databases that account for the industry’s large market share.
It is an open-source, massively parallel processing SQL database that is based on PostgreSQL. It is a database that’s is used for analytics. It is designed to manage large scale data warehouse and business intelligence workloads. This database model allows for access to a cluster of powerful servers that collaborate within a single SQL interface. It provides powerful and rapid analytics on data, thus enabling it to scale up to petabytes volumes of data. It provides the capability of running parallel queries against a huge amount of data.
Features of Greenplum
- Cloud-agnostic for flexible deployment in public cloud, private cloud, or on-premise
- Analytics from business intelligence to artificial intelligence
- Handle streaming data and enterprise ETL with ease
- Maximize uptime and protect data integrity
- Industry-leading performance
- Scales to petabytes of data
- Based on open-source projects like PostgreSQL
- Massively parallel, highly concurrent architecture
- Comes with libraries for advance analytics to process geospatial, text, machine learning, graph, time series, and artificial intelligence
- Has the capability of running on any platform
- Provides a industry leading Query Optimizer
- Has a high performance of data management and efficiency of stream data
- Tackles data from experimentation up to the huge deployment of data
- Trusted in enterprises for data warehousing in mission critical settings
Cassandra is a free and open-source database management tool created in 2008 by the Apache Software Foundation. It is a NoSQL DBMS that is mainly used to accommodate and manage a huge volume of data spread across many servers. Many enterprises and individuals worldwide mostly use databases due to their scalability and easy to accommodate more data and user requirements. It mainly works well under heavy workloads primarily due to its architecture design since it does have a single point of failure.
- Offers great scalability
- It has high fault tolerance
- Accommodate a high volume of data
- Simple Ring architecture
- No single point of failure
It doesn’t have a low-level locking feature
It has a different database design
More effort requires troubleshooting and maintenance
It is an open-source NoSQL databases management tool that offers high flexibility and scalability of data. It provides added convenience due to its querying and indexing capabilities. It was mainly designed to support enormous databases. This database tool is very compatible with many programming languages and supports multiple operating systems. Its main features include Aggregation, indexing replication, etc.
- It’s easy to learn
- Very reliable and low cost
- Provides support for multiple technologies and platforms
- Allows capability of data partitioning across multiple nodes
- It can store any data from text, arrays, Boolean, etc
- Provides cloud-based deployment solutions
- Has greater flexibility of configuration
- Has limited analytics
- It is somehow slow for certain use cases
It is one of the most widely used database management tools across the globe. MySQL originally developed it. It turns data into structured information in a wide array of applications. It was mainly designed to replace MySQL. It has in the past become scalable, fast, and robust for many businesses. It consists of a wide range of plugins, making it very versatile in many use cases. It provides an SQL interface for accessing the data. It uses a stored engine that functions as a transactional and non-transactional engine.
- It’s very compatible with many other languages that are mostly used with MySQL.
- Offers Tighter Security Measures due to its frequent updates
- Provides better storage engines
- Has a higher performance and efficiency
It is not scalable naturally to bigger data sets
It is not completely compatible with MySQL
5. Apache Hadoop
It is an open-source big data framework that is well known for its capability to have a huge scalable data processing capability. This big data tool can run on a prem or in the cloud. It requires low hardware requirements, thus making it easy to manage.
- It offers a very high configurable model of data processing
- It has the capability of resource scheduling and management
- It has a Hadoop library for enabling third-party modules
- Not a full SQL solution with ACID transactions
- Performance on advanced SQL is not ideal
- Not efficient in terms of space and complexity
- Scalability: The architectural design of the CouchDB makes it relatively adaptable when partitioning the databases into multiple nodes
- It has an HTTP AP that makes it easy for easy communicate
- It allows for first indexing and retrieval of information
- It is slower on memory than DBMS
- Replication of large databases may fail
- The JSON format of data consumes more storage
- It doesn’t support transactions
It is a relational open-source database management system that is very compatible with Oracle. It is a hybrid database that allows data to be stored and manipulated in memory alone, along with a physical disk or a through both. It will enable utilization of the server-side and client-side sharding and simultaneously improves the performance and compatibility. It is compatible and interoperable with other relational databases. Its key feature is the capability of high performance through in-memory capabilities.
- In-memory database
- Deployment flexibility
- Highly available
- It offers a rich and reliable suite of features
- Very flexible and user friendly
- Support both the disk memory and in-memory databases
- Offers accessibility across other platforms
It does not support server and client for Windows OS
It is an open-source distributed SQL query engine mainly used for interactive analytics queries against data sources. It was designed for interactive analytics and approached for commercial data warehouses. Presto was designed to combine data from multiple sources, thus allowing for analytics across the entire organization. Facebook mainly uses it.
- It offers Increased Efficiency
- Decent performance for OLAP
- It offers great support from the open-source community
It does not support ACID transactions due to the absence of a storage layer
It’s a Massive Parallel Processing open-source SQL query engine that is mainly used for processing huge volumes of data. It is primarily written C++ and Java. This data framework provides high performance and low latency as compared to other SQL engines. It combines the SQL support and a multi-user higher performance for the analytic databases, thus offering higher scalability and flexibility. It implements a distributed architecture using the daemon processes for query execution.
Features of Impala
- It supports various file formats, i.e., LZO, Avro
- It uses metadata and SQL syntax from Apache
- Provides faster access in HDFS
- It is faster in processing and execution of queries
- It does not require the transformation and movement of data
- Impala follows the Relational model, thus makes it easy to access
- Impala offers high performance and low latency for Hadoop
It doesn’t support indexing
It doesn’t support all data formats
It doesn’t have good locking and ACID support
It is an open-source database management system that is mainly used for column-oriented data. It deals with online analytical processing. It allows the generation of analytical reports using SQL queries that are usually updated in real-time. This database model is mainly characterized by high performance. It is relatively easy to work and has fault tolerance capability. It provides business enterprises the ability to add servers to their clusters without investing in more memory places or additional or modification of DBMS.
Features of Click house
- Allows linear scalability
- Has a high fault tolerance
- Provides a high performance of data procession
- It is very reliable
- Provides a true column-oriented DBMS
- Has the capability of handling trillions of columns
- Very scalable due to its fault tolerance
- It is easy to use
- Highly reliable
- Provides no support for transactions
- Does not have the capability of data modification
In conclusion, Big data analytics is increasingly widespread across the world with its incorporation in multiple industries from financial services to healthcare and government institutions. The open sources big data tools are the mainframe of big data implementation. Before selecting any database management tool, there is a need for one to have a good background in the various open-source tools. The rapid growth of information has provided a unique way for individuals and organizations to invest heavily in database management tools. There is a need to develop new capabilities for redefining traditional business models using large scale analytics.
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