Big data describes the large, challenging volumes of structured and unstructured data that inundate businesses daily. However, what organizations do with the data matters more than the type or volume of data. Big data analysis can produce insights that help decision-making and provide assurance when making critical business actions.
But when business analyst Doug Laney articulated the five V’s in the early 2000s, the concept of big data began to take off, which is now generally accepted as the definition of big data:
Volume. Organizations get information from various sources, including sales, Internet of Things (IoT) devices, machinery, social media, videos, photos, and audio. In the past, storing so much data would have been prohibitive, but data lakes, Hadoop, and the cloud have reduced that cost.
Velocity. Due to the growth of the Internet of Things, data is entering businesses at a previously unheard-of rate, and this data needs to be processed fast. The requirement to manage these data deluges in close to real-time is driven by RFID tags, sensors, and smart meters.
Variety. Different formats can store data, including unstructured text files, emails, videos, audio files, market ticker data, and traditional databases that store quantitative data.
Variability. Data flows are unpredictable, changing frequently and varying wildly in addition to the increasing velocities and varieties of data. Businesses must understand social media trends and how to handle peak daily, seasonal, and event-triggered data loads, which can be difficult.
Veracity. Data quality is referred to as veracity. It is challenging to link, match, clean up, and convert data across systems since it originates from many distinct sources. Relationships, hierarchies, and numerous data linkages need to be connected and correlated in business. If not, their data might easily spin out of control.
Big Data: Why Is It Important?
Big data’s significance is not just dependent on your data volume. Its worth depends on how you use it. Any data source can be used to gather information, which can then be analyzed to discover solutions that 1) simplify resource management, 2) boost operational effectiveness, 3) optimize product development, 4) provide new income and growth prospects, and 5) facilitate wise decision-making. Big data and high-performance analytics enable the completion of business-related tasks like:
- Identifying the underlying causes of problems, errors, and flaws in real-time.
- Quicker and more precisely detecting irregularities than the human eye.
- Enhancing patient outcomes by quickly extracting knowledge from medical picture data.
- Complete risk portfolios are recalculated rapidly.
- Improving the classification accuracy and responsiveness of deep learning models.
- Spotting fraud before it has an impact on your business.
Top Big Data tools:
The most well-known and commonly utilized extensive data framework is Apache Hadoop. Massive data collections can be distributedly and processed using Hadoop across computer clusters. One of the top Big Data Tools for scaling from a single server to tens of thousands of ordinary computers.
To coordinate distributed Big Data processing across a computer network, Hadoop is a free and open-source platform. Hadoop clusters numerous computers into a virtually infinitely scalable network and analyzes the data in parallel instead of storing and processing all of the data on a single machine.
A free and open-source software program for distributed processing is called Apache Spark. It links many computers together and enables parallel processing of Big Data, accelerating and streamlining Big Data activities. Due to its usage of machine learning and other technologies, which boosts its speed and efficiency, Spark is becoming increasingly popular.
Along with powerful APIs in Scala, Python, Java, and R, Spark has a range of tools that may be used for several features, such as the processing of structured data and graph data, Spark Streaming, and machine learning analysis.
A framework for distributed event processing or streaming called Apache Kafka enables applications to quickly process massive volumes of data. It can manage billions of events each day. It is a fault-tolerant streaming platform that is very scalable.
Like message systems, the streaming process involves posting and subscribing to streams of records, preserving them, and then analyzing them.
Another free platform for big data analytics that can handle unbounded data streams is Apache Storm. It supports JSON-based protocols and is a fault-tolerant, real-time processing system that works with all programming languages.
Despite its high processing rates and complexity, Apache Storm is very scalable and user-friendly.
Apache Cassandra is a non-relational database (NoSQL), providing large-scale, continuous availability and data dispersion across several data centers and Cloud availability zones. In a nutshell, Cassandra is a very reliable data storage engine for applications that require significant expansion.
It is commonly known that Apple has the largest deployment of the open-source Cassandra database. Another prominent usage of Apache Cassandra is Netflix.
A free and open-source Big Data software solution is Apache Hive. It helps Hadoop programmers to examine substantial data collections. It simplifies handling and querying large datasets. It performs SQL-like queries using the HQL (Hive Query Language), which are then internally converted to MapReduce tasks. Using Hive, you can avoid creating complex MapReduce programs the conventional way.
Small organizations can use Zoho Analytics, a convenient and affordable Big Data analytics solution. Its user-friendly interface lets you easily design complex dashboards and identify the most critical data.
While Zoho Analytics is a solid standalone option, it also has the advantage of being closely integrated with the other Zoho business tools, including CRM, HR, and marketing automation.
One of the quickest and safest big data tools accessible today is Cloudera. It began as a free version of Apache Hadoop designed for enterprise-level deployments. Data collection from every environment is made simple by this adaptable platform.
Software, support, and service bundles from Cloudera are available both on-premises and through several cloud service providers.
RapidMiner is another top-notch free big data analytics tool. It can manage model deployment, model development, and data preparation. It includes several add-ons to create unique data mining techniques and predictive setup analyses.
It offers several licenses for small, medium, and giant proprietary versions. It also seems to have a free version, although it only supports 10,000 data rows and 1 logical processor. Even when used in conjunction with Cloud services and APIs, it is highly effective because it was designed in Java. A variety of potent Data Science tools and algorithms are included.
One effective program frequently used for data purification and format transformation is OpenRefine. Large datasets are handled without any issues. It works with external data and expanded web services. Google Refine was its former name. Data is always kept private on your machine and can be shared with other team members, thanks to OpenRefine.
Kylin is a big data analytics platform and distributed data warehouse. It is based on Apache technologies like Spark, Parquet, Hive, and Hadoop. As a result of the OLAP engine it offers, it supports vast datasets.
Samza is an Apache-managed open-source distributed stream processing technology created by LinkedIn. It enables users to develop stateful programs for processing data in real-time from sources like Apache Kafka, HDFS, and other sources.
Large datasets can be analyzed and visualized using the free and open-source Lumify program. Users can produce insights by delving deeper into the data because of its user-friendly design.
One of the subsets of the Presto Query Engine, often known as PrestoSQL, is Trino. Trino natively executes queries in Hadoop and other data repositories, enabling users to query data regardless of where it is stored.
Integrate.io is a platform for integrating, processing, and getting data ready for cloud analytics. It will combine all of your data sources. Its user-friendly graphic interface will assist you in putting an ETL, ELT, or replication solution into place.
Integrate.io is a comprehensive toolkit with no-code and low-code features for creating data pipelines. It provides options for developers, support, sales, and marketing.
Utilizing Integrate.io will allow you to get the most out of your data without investing money in equipment, software, or associated personnel. Support is available from Integrate.io via email, chats, phone, and online meetings.
With the help of the adaptable end-to-end marketing analytics platform Adverity, marketers can easily find new insights in real-time while tracking marketing performance from a single perspective.
Adverity helps marketers track marketing success from a single perspective and quickly discover new real-time insights. This is made possible by automated data integration from over 600 sources, rich data visualizations, and AI-powered predictive analytics.
This leads to data-supported business decisions, more significant growth, and quantifiable ROI.
You may self-serve data intake, streaming, transformations, preparation, wrangling, reporting, and machine learning modeling with Dextrus’ assistance. Features consist of:
- Rapid insight into datasets: Using the capabilities of the Spark SQL engine, one of the components called “DB Explorer” assists in fast querying the data points to gain a solid understanding of the data.
- One method for identifying and ingesting modified data from source databases into the subsequent staging and integration levels is query-based CDC.
- Log-based CDC: Reading database logs to find ongoing changes to the source data is another way to accomplish real-time data streaming.
- Finding anomalies It’s frequently crucial to perform data pre-processing or data cleansing to provide the learning algorithm a valuable dataset to work with.
- Optimizing Push-down
- Easy data preparation
- analytics in every aspect
- Validation of Data
With a large selection of connectors and the freedom to select your own metrics and attributes, flexibility is a top priority for the cloud-based, no-coding ETL tool Dataddo. Dataddo makes building solid data pipelines quick and easy.
Dataddo simply integrates with your current data stack, so there’s no need to change your fundamental workflows or add components to your architecture that you weren’t already utilizing. You can concentrate on integrating your data rather than wasting time learning how to use yet another platform, thanks to Dataddo’s simple setup and straightforward UI.
Enterprise-class installations of that technology are what CDH strives for. It features a free platform distribution that includes Apache Hadoop, Apache Spark, Apache Impala, and many other open-source components.
It enables limitless data collection, processing, administration, management, discovery, modeling, and distribution.
Apache Cassandra is a distributed NoSQL DBMS that is open-source, free, and designed to manage large amounts of data spread over many commodity servers while ensuring high availability. CQL (Cassandra Structure Language) is used to communicate with the database.
Cassandra is used by Accenture, American Express, Facebook, General Electric, Honeywell, Yahoo, and other well-known companies.
Konstanz Information Miner, also known as KNIME, is an open-source program for business intelligence, data mining, CRM, integration, research, and enterprise reporting. Operating systems for Linux, OS X, and Windows are supported.
It can be viewed as a solid SAS substitute. Knime is widely used by prominent businesses like Comcast, Johnson & Johnson, Canadian Tire, etc.
An open-source data visualization tool called Datawrapper enables users to quickly build accurate, detailed, and embeddable charts.
The vast majority of its customers are newsrooms all throughout the world. Examples include The Times, Fortune, Mother Jones, Bloomberg, Twitter, and others.
MongoDB has several well-known users, including Facebook, eBay, MetLife, Google, and others.
A free and open-source tool for big data analytics and visualization is called Lumify.
Link analysis between graph parts, automatic layouts, full-text search, 2D and 3D graph visualizations, integration with mapping systems, geospatial analysis, multimedia analysis, and real-time collaboration through projects or workspaces are just a few key features.
High-Performance Computing Cluster is referred to as HPCC. This fully scalable supercomputing platform provides a big data solution in its entirety. DAS is another name for HPCC (Data Analytics Supercomputer). The company LexisNexis Risk Solutions created this technology.
This tool was created using the data-focused programming languages ECL and C++ (Enterprise Control Language). It is based on the Thor architecture, which provides a system, pipeline, and data parallelism. It’s an open-source solution that can effectively replace Hadoop and other big data systems.
Apache Storm is an open-source, fault-tolerant, distributed stream processing real-time computing framework. It is open-source and free. It is written in Java and Clojure. Backtype and Twitter are two of the developers behind the storm.
Its design is built on specialized spouts and bolts to represent information sources and manipulations, enabling batch, distributed processing of unlimited data streams.
Groupon, Yahoo, Alibaba, and The Weather Channel are well-known companies that use Apache Storm.
Scalable Advanced Massive Online Analysis is known as SAMOA. It is an open-source platform for big data stream mining and machine learning.
You can develop distributed streaming machine learning (ML) algorithms and use several DSPEs to perform them (distributed stream processing engines). BigML tool is the closest substitute for Apache SAMOA.
An autonomous and comprehensive Big data platform, Qubole Data Service controls, learns and optimizes itself based on your usage. This frees up the data team’s time to focus on achieving business goals rather than running the platform.
Among the numerous well-known brands that utilize Qubole are Gannett, Adobe, and the Warner Music Group. Revulytics is Qubole’s nearest rival.
Tableau is a business intelligence and analytics software platform that offers a range of integrated tools to help the most prominent organizations in the world visualize and comprehend their data.
There are three primary products in the software: Tableau Desktop (for analysts), Tableau Server (for businesses), and Tableau Online (to the cloud). Additionally, two additional products have just been added: Tableau Reader and Tableau Public.
Tableau offers you real-time customizable dashboards and can handle any data quantities. It is an excellent tool for exploring and visualizing data. It is also simple to use for both technical and non-technical customers.
One of the most complete statistical analysis software tools is R. It is a dynamic, open-source, accessible, and multi-paradigm software environment. C, Fortran, and R programming languages were used to create it.
Data miners and statisticians both frequently utilize it. Its use cases include data analysis, manipulation, calculation, and graphical display.
A distributed, RESTful, open-source, cross-platform search engine built on Lucene, elastic search.
One of the most widely used business search engines is it. When combined with Logstash (a data collection and log parsing engine) and Kibana (an analytics and visualization platform), it offers a comprehensive solution known as an elastic stack.
OpenRefine is a free, open-source program for managing untidy data, cleaning it up, converting it, expanding it, and enhancing it. Platforms for Windows, Linux, and macOS are supported.
Research software all in one is called Atlas.ti. It can be utilized for mixed techniques and qualitative data analysis in academic, market, and user experience research. With this big data analytical tool, you can access all available platforms from one place.
Note: We tried our best to feature the Big Data Tools, but if we missed anything, then please feel free to reach out at Asif@marktechpost.com Disclaimer: We make a small profit from purchases made via referral/affiliate links linked with premium books, courses, hardwares etc.
Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications