big data open source tools

Big Data – the powerful tool for today’s workforce, is continually helping to transform and translate massive amounts of structured and unstructured information into valuable business insights. Big data tools, which are the key weapons to make this translation or transformation happen, come either as big data open-source tools or commodity software. Not to mention, open-source big data tools for big data analysis have flooded the market, and they have to capture the major market share of big data tools. Furthermore, these big data open-source tools offer endless functionalities and provide business insights and forecasting in a cost-efficient and time-saving manner.

This blog covers 10 such big data open-source tools, including big data reporting tools, open-source and big data visualization tools open source tools. Let’s explore!

#1. Apache Hadoop

If we start talking about big data open-source tools, then the first and the foremost name pops up in mind is Apache Hadoop. This software framework for Big data analytics tools open source category employs a clustered file system and efficiently handles all kinds of big data. MapReduce is the key programming model here that processes large data sets. Among other open-source tools for big data, Hadoop is written in Java and provides cross-platform support.

Pros:

  • Hadoop HDFS (Hadoop Distributed File System) can hold all types of data – images, video, JSON, plain text, XML over the same file system.
  • Hadoop distributes data and computation. Thus the local computation prevents the network overload.
  • Tasks run as independent entities.
  • It provides quick access to data.
  • It follows a simple programming model through Map-reduce.
  • Highly useful for R&D purposes.
  • Highly fault-tolerant.
  • Highly scalable, and it is flat scalability.
  • Highly-available service resting on a cluster of computers

Cons:

  • Its architecture and framework are complex, so managing the Hadoop framework is challenging, especially from the data security point of view.
  • Sometimes it causes disk space issues due to its 3x data redundancy.
  • It has some potential stability issues for some versions.

Link –  http://hadoop.apache.org/

Related post - Top 5 Big Data Frameworks In 2020

#2. Apache Cassandra

Apache Cassandra is one of the Big data open-source tools that fall under the distributed NoSQL DBMS category. It is highly efficient to manage huge volumes of data spread across numerous commodity servers and deliver high availability. This tool employs CQL (Cassandra Structure Language) to interact with the database. It is linearly scalable and has proven fault-tolerance on both commodity hardware and in cloud infrastructure. Thus it is ideal for mission-critical data. It supports replication across multiple data centers, provides lower latency. So, as a user, you can avoid regional outages.

Pros:

  • Highly fault-tolerant.
  • Benchmark software for handling massive data very quickly.
  • Decentralized as there is no single point of failure.
  • Linear scalability
  • Simple elastic architecture
  • Durable

Cons:

  • Sometimes troubleshooting and maintenance become an issue.
  • Need improved clustering.
  • There is no row-level locking feature available.

Link - http://cassandra.apache.org/

#3. Knime

Suppose you are looking for Big data reporting tools open source category, then KNIME first. KNIME stands for Konstanz Information Miner and is used for Enterprise reporting, research, integration, data mining, CRM, data analytics, text mining, and business intelligence. KNIME allows performing sophisticated statistics and data mining on big data. It has a visual workbench that combines data access, data transformation, initial analysis, predictive analysis, etc. It supports Linux, OS X, and Windows operating systems. What KNIME do for you?

  • Gather and wrangle
  • Model and visualize
  • Deploy and manage
  • Consume and optimize

Pros:

  • It follows simple ETL operations.
  • You can integrate KNIME with other technologies and languages.
  • It consists of a rich algorithm set.
  • Highly organized workflows.
  • Good automation software.
  • No stability issues.
  • Easy to set up.
  • Intermediate results can be analyzed.

Cons:

  • Data handling capacity can be improved.
  • RAM consumption is high.
  • Could have allowed integration with graph databases.

Link - https://www.knime.com/software-overview

#4. Datawrapper

Datawrapper is a Big data visualization tool open-source that aids its users in generating simple, beautiful, precise embeddable charts, maps, and tables quickly. It helps to create digitally optimized visual charts, maps, or tables. No need to install or rebuild if a chart is crashed. It is fully responsive. It comes with good graphic design by default. Thus it is easy to make understanding the charts. Also, the user can perform custom styling on the charts, maps, or tables. Besides, you can export the charts in many formats like pdf, HTML, etc. Datawrapper can facilitate collaboration. Hence, it can be integrated with other collaboration tools like Slack, etc.

Pros:

  • Device friendly. Works very well on all types of devices – mobile, tablet, or desktop.
  • Fully responsive
  • Fast
  • Live updating graphics.
  • Interactive
  • Brings all the charts in one place.
  • Great customization and export options.
  • It requires zero codings.
  • Can perform CMS integration

Cons: Limited color palettes

Link - https://www.datawrapper.de/

#5. Lumify

Another Big data visualization tool open source category is Lumify, which can perform Big data fusion or integration, analytics, and visualization. It provides graph visualizations, full-text faceted search, dynamic histograms, interactive geospatial views, and collaborative workspaces shared in real-time. It has a cloud-based architecture.

Pros:

  • Speed and Scalable
  • Secure
  • Brings Your Own Analytics Capability
  • Supported by a dedicated full-time development team.
  • Real-Time and Secure Collaboration
  • Works well with Amazon’s AWS

Link - https://www.altamiracorp.com/lumify-slick-sheet/

#6. Apache Storm

Apache Storm is one of the Open source tools for big data, which is cross-platform, distributed, supports stream processing of Big data. It comes with a highly fault-tolerant real-time computational framework. It is written in Clojure and Java. Storm supports real-time processing. Apache Storm's spout abstraction makes it easy to integrate a new queuing system.

Pros:

  • Reliable at scale.
  • Supports any programming language.
  • Very fast and fault-tolerant.
  • Integrates with any queuing system and any database system. 
  • Guarantees the processing of data.
  • It has multiple use cases – real-time analytics, log processing, ETL (Extract-Transform-Load), continuous computation, distributed RPC, machine learning.

Cons:

  • Difficult to learn and use.
  • Difficulties with debugging.
  • The use of Native Scheduler and Nimbus become bottlenecks.

Link - http://storm.apache.org/

#7. Talend

Talend's big data solution is one of the big data open-source tools that can scale big data anywhere – in the cloud, hybrid infrastructure, and on-premises. The open-source tool for Big data from Talend is Open studio, which consists of Hadoop and NoSQL. It only provides community support.

Features

Components

  • Hadoop components: HDFS, Hbase, Hive, Pig, Sqoop
  • File management: open, move, compress, decompress without scripting.
  • Control and orchestrate data flows and data integrations with master jobs
  • Map, aggregate, sort, enrich, and merge data

License and Support

  • Free open source Apache license

Design and Productivity Tools

  • Generates native MapReduce and Spark batch code
  • Visual mapping for complex JSON, XML, and EDI on Spark
  • Spark and MapReduce job designer
  • Serverless Spark processing through Databricks and Qubole
  • Dynamic distribution support
  • Hadoop job scheduler with YARN
  • Hadoop security for Kerberos
  • Ingestion, loading, and unloading data into a data lake
  • Graphical design environment
  • Team collaboration with a shared repository
  • Continuous integration / Continuous delivery
  • Visual mapping for complex JSON, XML, and EDI
  • Audit, job compare, impact analysis, testing, debugging, and tuning
  • Metadata bridge for metadata import/export and centralized metadata management
  • Distant run and parallelization
  • Dynamic schema, re-usable joblets, and reference projects
  • Repository manager
  • ETL and ELT support
  • Wizards and interactive data viewer
  • Versioning
  • Change data capture (CDC)
  • Automatic documentation
  • Customizable assessment
  • Pattern library
  • Cloud Pipeline Designer

Connectors

  • Cloud: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and more
  • Cloud Data Warehouse and Data Lakes: Snowflake, Amazon Redshift, Azure Data Lake Storage Gen2, Azure SQL Data Warehouse, Databricks Delta Lake, Google BigQuery
  • Supported big data distributions: Amazon EMR, Azure HDInsight, Cloudera, Google Dataproc, Hortonworks, MapR
  • Serverless: Cloudera Altus, Databricks, Qubole
  • Spark MLlib (classification, clustering, recommendation, regression)
  • NoSQL: Cassandra, Couchbase, DynamoDB, MongoDB, Neo4j, and more
  • RDBMS: Oracle, Teradata, Microsoft SQL Server, and more
  • SaaS: Marketo, Salesforce, NetSuite, and more
  • Packaged Apps: SAP, Microsoft Dynamics, Sugar CRM, and more
  • Technologies: Dropbox, Box, SMTP, FTP/SFTP, LDAP, and more
  • Optional 3rd-party address validation services

Management and Monitoring

  • High availability, load balancing, failover for jobs
  • Deployment manager and team collaboration
  • Manage users, groups, roles, projects, and licenses
  • Manage execution engines
  • Single Sign-On (SSO) integration with several SSO providers
  • The execution plan, time, and event-based scheduler for jobs
  • Checkpoints, error recovery
  • Context management (dev, QA, prod)
  • Log collection and display
  • Optional Admin user add-on*
  • Engine clusters for jobs*
  • Static IP addresses*
  • Job execution log history (2 months for Entry products, 3 months for Platforms)*
  • Environments (2 for Entry products, unlimited for Platforms)*
  • Cloud Security Information and Event Management (SIEM), Intrusion Detection System (IDS), Intrusion Prevention System (IPS) and Web Application Firewall (WAF)

Big Data Quality

  • Data cleansing, profiling, masking, parsing, and matching on Spark and Hadoop
  • Machine learning for data matching and deduplication
  • Support for Cloudera Navigator and Apache Atlas
  • HDFS file profiling

Pros:

  • Streamlines ETL and ELT for Big data.
  • Accomplish the speed and scale of spark.
  • Accelerates your move to real-time.
  • Handles multiple data sources.
  • It provides numerous connectors under one roof, which will allow you to customize the solution as per your need.

Cons:

  • Community support could have been better.
  • Could have an improved and easy to use interface
  • Difficult to add a custom component to the palette.

Link - https://www.talend.com/products/big-data/

#8. Apache SAMOA

Another popular Big data open-source tool is SAMOA, which stands for Scalable Advanced Massive Online Analysis. This open-source platform is used for big data stream mining and machine learning. Its pluggable architecture allows it to run on several DSPEs such as Apache Storm, Apache Samza, Apache S4, and Apache Flink. You can create distributed streaming machine learning (ML) algorithms with it.

Pros:

  • Simple and easy to use.
  • Fast and scalable.
  • True real-time streaming.
  • Write Once Run Anywhere (WORA) architecture.

Link - https://samoa.incubator.apache.org/

#9. MongoDB

MongoDB is a NoSQL, general-purpose, document-oriented, database written in C, C++, and JavaScript. It is one of the open-source big data tools which open-source tool that supports multiple operating systems. MongoDB uses powerful query language which supports aggregations and other modern use-cases such as geo-based search, graph search, and text search. It contains the property of fully RDBMS. MongoDB Atlas, which is a global cloud database, works well with all major cloud platforms. Besides, it provides JSON documents.

Pros:

  • Easy to learn.
  • Supports multiple technologies and platforms.
  • Straightway installation and maintenance.
  • Reliable and low cost.

Cons:

  • Limited scope for analytics.
  • Slow for certain use cases.

Link - https://www.mongodb.com/

#10. Spark

Apache Spark is probably the most important among big data open-source tools other than Hadoop comes with a unified analytics engine for processing large scale data. Spark is well known for its speed, which is what Hadoop lacks. Due to its state of the art DAG scheduler, Spark can process data 100x faster. It is equally efficient to process batch as well as streaming data. One of the prime features of Spark is it can run anywhere, like on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud.

Spark contains a library called MLib that offers machine learning algorithms.

Pros

  • Helps to execute software in Hadoop cluster
  • Provides lighting Fast Processing
  • Supports complex analytics
  • Accommodates Hadoop and its existing data
  • Provides inbuilt APIs in Java, Python, Scala or R and SQL.

Link - https://spark.apache.org/

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