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Top 15 Big Data Tools | Open Source Software for Data Analytics

Updated: Apr 20, 2023

In this article, we'll be discussing 15 popular tools for data analytics that use open-source software. These tools are designed to handle big data and are widely used in the industry for various purposes, such as data cleansing, data processing, data visualization, and more. If you're looking to get started with big data analytics or want to expand your knowledge in this area, this article will give you an overview of some of the most popular and useful tools available.


















1) Hadoop:

Hadoop is an open-source, Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. It allows distributed processing of large data sets across clusters of computers. It is one of the best big data tools designed to scale up from single servers to thousands of machines.


It consists of four main components:

  1. Hadoop Distributed File System (HDFS),

  2. MapReduce, YARN, and

  3. Hadoop Common

HDFS is a distributed file system that stores data across multiple nodes in a cluster. MapReduce is a programming model that allows parallel processing of data using two functions: map and reduces. YARN is a resource management layer that allocates CPU and memory resources to different applications running on the cluster. Hadoop Common is a set of libraries and utilities that support the other components


Features:

  • Authentication improvements when using HTTP proxy server

  • Specification for Hadoop Compatible Filesystem effort

  • Support for POSIX-style filesystem extended attributes

  • It has big data technologies and tools that offer robust ecosystem that is well-suited to meet the analytical needs of the developer

  • It brings Flexibility In Data Processing

  • It allows for faster data Processing

Pros

Cons

Hadoop can scale up to thousands of nodes and handle petabytes of data

Hadoop requires a steep learning curve and a lot of technical expertise to set up, configure, and maintain.

Hadoop runs on commodity hardware, which reduces the cost of infrastructure and maintenance. It also uses data compression and replication techniques to optimize storage space and availability.

It has a low-level programming interface that can be challenging for developers who are not familiar with MapReduce or Java.

Hadoop can handle any type of data, whether structured, semi-structured, or unstructured. It can also support various data formats, such as text, image, video, audio, etc.

Hadoop has limited security features, such as authentication, authorization, encryption, and auditing. It relies on external tools and frameworks, such as Kerberos, Ranger, Knox, etc., to provide additional security layers. However, these tools can add more complexity and overhead to the system.

Hadoop replicates data across multiple nodes in a cluster, which ensures data availability and reliability in case of node failure or network outage.

Hadoop requires constant monitoring and tuning to ensure optimal performance and resource utilization. It also requires regular updates and patches to fix bugs and security issues.



2) HPCC:

HPCC (High-Performance Computing Cluster), is an open-source, distributed processing framework that is designed to handle big data analytics. It was developed by LexisNexis Risk Solutions as an alternative to Hadoop.


It consists of two main components:

  1. Thor and

  2. Roxie.

Thor is a data refinery cluster that performs batch data processing, such as extraction, transformation, loading, cleansing, linking and indexing. Roxie is a rapid data delivery cluster that provides online query delivery for big data applications using indexed data files.


HPCC also includes a high-level, declarative programming language called Enterprise Control Language (ECL), which is used to write parallel data processing programs


Features:

  • It is one of the Highly efficient big data tools that accomplish big data tasks with far less code.

  • It is one of the big data processing tools which offers high redundancy and availability

  • It can be used both for complex data processing on a Thor cluster

  • Graphical IDE for simplifies development, testing and debugging

  • It automatically optimizes code for parallel processing

  • Provide enhance scalability and performance

  • ECL code compiles into optimized C++, and it can also extend using C++ libraries

Pros

Cons

Highly integrated system environment that includes data storage, processing, delivery and management in a single platform. It also supports seamless data integration from various sources and formats.

Not fully compatible with Hadoop and its ecosystem of tools and frameworks. It has limited support for popular languages, such as Java, Python and R. It also has limited interoperability with other big data platforms and databases.

Can handle any type of data, whether structured, semi-structured or unstructured. It can also support various data analysis techniques, such as machine learning, natural language processing, graph analytics, etc.

Has a smaller and less active community than Hadoop. It has fewer resources, documentation and tutorials available online. It also has fewer contributors and users who can provide feedback and support.

Has built-in security features, such as authentication, authorization, encryption and auditing. It also has recovery and backup mechanisms that ensure data availability and reliability.


3) Storm:

Storm is an open-source, distributed processing framework that is designed to handle real-time streaming data. It was developed by BackType and later acquired by Twitter. It works by defining data streams and processing them in parallel using spouts and bolts. Spouts are sources of data streams, such as Twitter feeds, Kafka topics, etc. Bolts are units of processing logic that can perform operations on data streams, such as filtering, aggregating, joining, etc. Storm can be integrated with various tools and frameworks, such as Hadoop, Spark, Kafka, Cassandra, etc


Features:

  • It is one of the best tools from the big data tools list which is benchmarked as processing one million 100-byte messages per second per node

  • It has big data technologies and tools that use parallel calculations that run across a cluster of machines

  • It will automatically restart in case a node dies. The worker will be restarted on another node

  • Storm guarantees that each unit of data will be processed at least once or exactly once

  • Once deployed Storm is surely the easiest tool for Bigdata analysis

Pros

Cons

Storm guarantees that each tuple in a data stream will be processed at least once or exactly once, depending on the configuration.

Storm requires constant monitoring and tuning to ensure optimal performance and resource utilization.

It can also support various programming languages, such as Java, Python, Ruby, etc.

It relies on external tools and frameworks, such as Kerberos, ZooKeeper, etc., to provide additional security layers.

It also uses a graph-centric programming model that optimizes data flow and parallelism.

It has limited support for popular formats, such as Parquet, Avro, etc.


4) Qubole:

Qubole is a cloud-based service that provides a data lake platform for big data analytics. It was founded by former Facebook engineers and is based in California. Qubole allows users to run data pipelines, streaming analytics and machine learning workloads on any cloud, such as AWS, Azure, Google Cloud, etc. Qubole also supports various tools and frameworks, such as Hadoop, Spark, Hive, Presto, Airflow, etc. Qubole aims to simplify and automate the management and optimization of big data infrastructure and resources


Features:

  • Single Platform for every use case

  • It is an Open-source big data software having Engines, optimized for the Cloud

  • Comprehensive Security, Governance, and Compliance

  • Provides actionable Alerts, Insights, and Recommendations to optimize reliability, performance, and costs

  • Automatically enacts policies to avoid performing repetitive manual actions

Pros

Cons

Qubole uses a pay-per-use model that charges users based on their actual consumption of cloud resources.

It has a low-level programming interface that can be challenging for developers who are not familiar with big data concepts and tools.

It also uses intelligent auto-scaling and spot instances to optimize resource utilization and reduce costs.

Qubole has limited security features, such as encryption at rest and in transit

Qubole provides a user-friendly interface that allows users to easily create, manage and monitor their big data projects.

Qubole is not fully compatible with some cloud platforms and services, such as AWS EMR, Azure HDInsight, Google Dataproc, etc.


5) Cassandra:

Cassandra is a big data tool that is a no-SQL database from Apache that can store and process large amounts of data across multiple servers. It is an open-source, distributed, and scalable system that offers high availability, reliability, and performance. It is suitable for applications that require fast and reliable data access and can handle data center outages. It is used by thousands of companies for various use cases such as social media, e-commerce, analytics, etc


Features:

  • Support for replicating across multiple data centers by providing lower latency for users

  • Data is automatically replicated to multiple nodes for fault-tolerance

  • It is one of the best big data tools which are most suitable for applications that can't afford to lose data, even when an entire data center is down

  • Cassandra offers support contracts and services are available from third parties

Pros

Cons

Offers highly-available service and no single point of failure

It has steep learning curve and complex configuration

It can handle massive volume of data and fast writing speed

It does not support joins, transactions or aggregations

It has flexible data model and supports various data types. It can easily scaled or expanded without affecting the performance

It may have consistency issue due to eventual consistency model. It may also have high hardware and maintenance costs.


6) Statwing:

Statwing is a big data tool that is web-based software for data analysis and visualization. It is designed to make data exploration and presentation easy and intuitive for users without coding or statistical skills.


Features:

  • It is a big data software that can explore any data in seconds

  • Statwing helps to clean data, explore relationships, and create charts in minutes

  • It allows the creation of histograms, scatterplots, heatmaps, and bar charts that export to Excel or PowerPoint

  • It also translates results into plain English, so analysts unfamiliar with statistical analysis

Pros

Cons

It has user-friendly interface and natural language output

It is not free and required a subscription fee

It can handle various data types and formats.

It has limited customization and advanced analysis options

It can perform various statistical tests and generate charts and graphs. It can also export results to Excel, PowerPoint or PDF

It may not support very large datasets or complex queries. It may also not integrate well with other tools or platforms


7) CouchDB:

CouchDB stores data in JSON documents that can be accessed web or query using JavaScript. It offers distributed scaling with fault-tolerant storage. It allows accessing data by defining the Couch Replication Protocol.


Features:

  • CouchDB is a single-node database that works like any other database

  • It is one of the big data processing tools that allows running a single logical database server on any number of servers

  • It makes use of the ubiquitous HTTP protocol and JSON data format

  • Easy replication of a database across multiple server instances

  • Easy interface for document insertion, updates, retrieval and deletion

  • JSON-based document format can be translatable across different languages

Pros

Cons

It is open-source and cross-platform tool

It may not perform well with complex queries or large datasets

It has a flexible schema and supports various data types

It may have consistency issues due to eventual consistency model

It can scale horizontally and provide high availability

It may not support PostgreSQL or other relational database.


8) Pentaho:

Pentaho is a big data tool that is a suite of open-source business intelligence and analytics products from Hitachi Data Systems. It provides data integration, reporting, analysis, data mining, and dashboard capabilities.


Features:

  • Data access and integration for effective data visualization

  • It is a big data software that empowers users to architect big data at the source and stream them for accurate analytics

  • Seamlessly switch or combine data processing with in-cluster execution to get maximum processing

  • Allow checking data with easy access to analytics, including charts, visualizations, and reporting

  • Supports wide spectrum of big data sources by offering unique capabilities

Pros

Cons

It supports various data sources, such as Hadoop, Spark, NoSQL, and relational databases.

It may have performance issues when handling large volumes of data or complex transformations.

It has a graphical user interface that simplifies data preparation and blending tasks.

It may require some coding skills to customize or extend its functionality.

It offers a range of analytics features, such as reporting, dashboards, OLAP, and data mining.

It may have compatibility issues with some newer versions of big data frameworks or platforms.


9) Flink:

Flink is a distributed streaming dataflow engine that provides high performance, low latency, and fault tolerance for big data applications. It can process data from various sources, such as Hadoop, Kafka, Cassandra, and Amazon Kinesis, and deliver it to various sinks, such as HDFS, Elasticsearch, and MySQL. Flink supports various types of processing, such as batch processing, interactive processing, stream processing, iterative processing, in-memory processing, and graph processing. Flink is based on a streaming model that allows it to handle both finite and infinite data streams efficiently. It also provides advanced features, such as state management, checkpointing, savepoints, and windowing.


Features:

  • Provides results that are accurate, even for out-of-order or late-arriving data

  • It is stateful and fault-tolerant and can recover from failures

  • It is a big data analytics software that can perform at a large scale, running on thousands of nodes

  • Has good throughput and latency characteristics

  • This big data tool supports stream processing and windowing with event time semantics

  • It supports flexible windowing based on time, count, or sessions to data-driven windows

  • It supports a wide range of connectors to third-party systems for data sources and sinks

Pros

Cons

It can handle both batch and stream processing with a unified API and rutime

Steep learning curve for beginners and require some programming skills to use effectively.

It can provide strong consistency and fault tolerance guarantees with its snapshot mechanism

It may have some compatibility issues with some newer versions or features of big data frameworks or platforms.

It can support complex and iterative algorithms such as machine learning and graph processing.

It may have some limitations or trade-offs in terms of memory management, resource allocation, and performance tuning.


10) Cloudera:

Cloudera is a data platform that enables organizations to securely store, process, and analyze large volumes of data across public and private clouds. Cloudera offers an open data lakehouse powered by Apache Iceberg that combines the best of data lakes and data warehouses. Cloudera also offers a streaming data platform that connects to any data source and delivers data to any destination in real-time. Cloudera supports various types of analytics, such as batch processing, interactive processing, stream processing, machine learning, and artificial intelligence. Cloudera provides unified security and governance for data and workloads with its Shared Data Experience (SDX) feature. Cloudera also provides professional services, training, and support for its customers.


Features:

  • High-performance big data analytics software

  • It offers provision for multi-cloud

  • Deploy and manage Cloudera Enterprise across AWS, Microsoft Azure and Google Cloud Platform

  • Spin up and terminate clusters, and only pay for what is needed when need it

  • Developing and training data models

  • Reporting, exploring, and self-servicing business intelligence

  • Delivering real-time insights for monitoring and detection

  • Conducting accurate model scoring and serving

Pros

Cons

It provides a comprehensive and flexible data platform that can handle any type of data and analytics.

It may have a high learning curve and require some technical skills to use effectively.

It leverages open-source technologies and standards that are widely used and supported by the community.

It may have some compatibility issues with some newer versions or features of open-source projects or platforms.

It offers cloud-native solutions that are scalable, reliable, and cost-effective.


11) Openrefine:

Open Refine is a powerful big data tool. It is a big data analytics software that helps to work with messy data, cleaning it and transforming it from one format into another. It also allows extending it with web services and external data.


Features:

  • OpenRefine tool helps you explore large data sets with ease

  • It can be used to link and extend your dataset with various web services

  • Import data in various formats

  • Explore datasets in a matter of seconds

  • Apply basic and advanced cell transformations

  • Allows to deal with cells that contain multiple values

  • Create instantaneous links between datasets

  • Use named-entity extraction on text fields to automatically identify topics

  • Perform advanced data operations with the help of Refine Expression Language


12) Rapidminer:

RapidMiner is one of the best open-source data analytics tools. It is used for data prep, machine learning, and model deployment. It offers a suite of products to build new data mining processes and set up predictive analysis.


Features:

  • Allow multiple data management methods

  • GUI or batch processing

  • Integrates with in-house databases

  • Interactive, shareable dashboards

  • Big Data predictive analytics

  • Remote analysis processing

  • Data filtering, merging, joining and aggregating

  • Build, train and validate predictive models

  • Store streaming data to numerous databases

  • Reports and triggered notifications


13) DataCleaner:

DataCleaner is a data quality analysis application and a solution platform. It has a strong data profiling engine. It is extensible and thereby adds data cleansing, transformations, matching, and merging.


Feature:

  • Interactive and explorative data profiling

  • Fuzzy duplicate record detection

  • Data transformation and Standardization

  • Data validation and reporting

  • Use of reference data to cleanse data

  • Master the data ingestion pipeline in the Hadoop data lake

  • Ensure that rules about the data are correct before user spends their time the processing

  • Find the outliers and other devilish details to either exclude or fix the incorrect data


14) Kaggle:

Kaggle is the world's largest big data community. It helps organizations and researchers post their data & statistics. It is the best place to analyze data seamlessly.


Features:

  • The best place to discover and seamlessly analyze open data

  • Search box to find open datasets

  • Contribute to the open data movement and connect with other data enthusiasts



15) Hive:

Hive is an open-source big data software tool. It allows programmers to analyze large data sets on Hadoop. It helps with querying and managing large datasets really fast.


Features:

  • It Supports SQL like query language for interaction and Data modeling

  • It compiles language with two main tasks map and reducer

  • It allows defining these tasks using Java or Python

  • Hive is designed for managing and querying only structured data

  • Hive's SQL-inspired language separates the user from the complexity of Map Reduce programming

  • It offers Java Database Connectivity (JDBC) interface




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