“Big Data” is one of the most used terms in technology. With the volume of data created every minute by customers and organizations around the world, Big Data Analytics has a lot of promise. Hence, large amounts of useful data generated by businesses need to be managed, stored, visualized, and analyzed.
Since traditional data tools aren’t built to handle this level of complexity and volume, a plethora of specialized Big Data software tools and architectural solutions have emerged to handle this workload.
Businesses can use custom-built Big Data Tools to put their data to work, find new opportunities, and establish new business models. In this blog, you will understand the need for Big Data Tools and Technologies. In addition, you will learn the important factors to keep in mind while selecting the right Big Data Tool for your organization.
Table of Contents
- What is Big Data?
- The Need for Big Data Tools and Analytics
- Factors to Consider while Selecting the Big Data Tools
- Best Big Data Tools in the Market
- Key Benefits and Use Cases of Big Data Tools
What is Big Data?
Big Data refers to vast, diversified amounts of data that are growing at an exponential rate. It is described as “Big” not just because of its size, but also because of its enormous diversity and complexity. Its’ capacity to acquire, organize, and process it typically exceeds that of traditional databases.
Big Data can also come from anywhere on the planet that we can monitor digitally. While there are several definitions for Big Data, the majority of them involve the notion of the “5 V’s” of Big Data:
- Volume: It is important to consider the amount of data available. You’ll have to analyze a lot of low-density, unstructured data. The volume of Big Data can be tens of gigabytes of data for certain businesses or hundreds of petabytes for others.
- Velocity: The pace at which data is received and acted on is referred to as velocity. In most cases, data is streamed directly into memory rather than being written to the disc. Some internet-connected smart devices function in real-time or near-real-time, necessitating real-time evaluation and response.
- Variety: The different types of data that are available are referred to as variety. Traditional data formats were well-structured and fit into a relational database with ease. With the growth of Big Data, new unstructured data kinds have emerged. To infer meaning and support metadata, unstructured and semi-structured data formats like text, audio, and video require further preprocessing.
- Veracity: The models constructed on the data will not be of actual value without this attribute, given the volume, diversity, and pace that Big Data provides. The credibility of the original data, as well as the quality of the data generated after processing, is referred to as veracity. Data biases, abnormalities or inconsistencies, volatility, and duplication, among other factors, should be mitigated by the system.
- Value: Value is the most essential V in the business world. An enormous volume of information is produced daily, but collecting data is not the mere solution for businesses. Organizations invest in several Big Data technologies, as it not only facilitates Data Aggregation and Storage but also assists in garnering insights from raw data that could help companies gain a competitive edge in the market.
The Need for Big Data Tools and Analytics
Big Data Tools are used to extract information from a vast number of data sets and process them. The value of Big Data isn’t only determined by the amount of data available.
Its worth is determined by how you use it. The Big Data ecosystem is evolving at a breakneck speed. Multiple functions within the business are now supported by a broad range of analytic approaches.
- Users can leverage Descriptive Analytics to figure out “what happened and why.” Traditional query and reporting settings with scorecards and dashboards are examples of this type of Analytics.
- Users can use Predictive Analytics to assess the probability of a specific occurrence in the feature. Early warning systems, fraud detection, preventative maintenance applications, and forecasting are just a few examples.
- Prescriptive Analytics gives the user precise (prescriptive) recommendations. They respond to the query, “What should I do if “x” occurs?”
You can easily complete business-related activities when you integrate Big Data with high-performance Analytics. In traditional databases, processing a vast volume of data can be quite challenging.
As a result, you can easily use Big Data Tools to manage your data. Big Data Analytics can help you make better and quicker decisions, model and forecast future events, and improve your Business Intelligence.
Factors to Consider while Selecting the Big Data Tools
The ideal Big Data Tool for your company will be tailored to your specific requirements. Big Data integration solutions have the potential to significantly simplify this process. The following are characteristics to look for in a Big Data Tool:
- Understand Your Business Objectives: Like any other IT investment, your Big Data Analytics platform should be able to meet both current and future business demands. To begin, identify your company’s essential goals and create a list of targeted business outcomes. After that, break down your business objectives into quantitative analytics goals. Finally, choose Big Data Tools that offer you access to data and reporting capabilities that will help you achieve your business objectives.
- Keep a Check on Cost: Before choosing a Big Data Tool, you must be thoroughly aware of all costs associated with the solution you are considering, including memberships, growth, and additional expenditures. Different Big Data Tools and technologies have different pricing structures, which you should be aware of before you buy.
- Simple User Interface: Big Data Tools must be user-friendly and adaptable to a wide range of users. Even non-technical employees should be able to easily create and understand dashboards and reports. While aesthetics may not appear to be important, having unappealing graphics on your panels will reduce user adoption.
- Advanced Analytics: Your Big Data Analytics Tool should be able to discover patterns in data and forecast future events and outcomes. It must go beyond simple mathematical calculations to deliver contextually relevant insights that will allow you to build complicated forecasting algorithms and future-proof your business.
- Seamless Integrations: When selecting a Big Data Tool, you must decide if a standalone or integrated solution is ideal for your firm. You have a variety of options with standalone solutions, but with integrated solutions, you can get Analytics from apps that your employees are already familiar with. You need to be able to connect your Analytics platform to your existing systems as well as third-party data sources.
- Agile and Scalable: Cloud-based Big Data Tools are designed to start small and expand along with your business. These compensation plans can help early-stage companies acquire a competitive advantage and stay afloat during periods of fast growth. You can gain faster data access and Analytics to make quick decisions that expand by your company’s needs.
- High Security: The Big Data you are dealing with might have sensitive information. Hence, you must evaluate the security of your Big Data Tools to ensure that adequate safeguards are in place to secure your data. To limit which individuals or groups have access to which data, establish standard security protocols and procedures at all levels, processes, systems, and data.
Best Big Data Tools in the Market
In this section, you will explore the most popular and robust Big Data Tools available in the market. Explore the following powerful Big Data Tools along with their key features below:
1) Hevo Data
Hevo allows you to replicate data in near real-time from 150+ sources to the destination of your choice including Snowflake, BigQuery, Redshift, Databricks, and Firebolt. Without writing a single line of code. Finding patterns and opportunities is easier when you don’t have to worry about maintaining the pipelines. So, with Hevo as your data pipeline platform, maintenance is one less thing to worry about.
For the rare times things do go wrong, Hevo ensures zero data loss. To find the root cause of an issue, Hevo also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility. Check Hevo’s in-depth documentation to learn more.
If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest upto 1 million records.
Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.– Juan Ramos, Analytics Engineer, Ebury
Check out how Hevo empowered Ebury to build reliable data products here.Sign up here for a 14-Day Free Trial!
2) Apache Hadoop
Apache Hadoop is the most popular and widely used Big Data framework in the market. Hadoop allows for distributed processing of massive data sets across clusters of computers. It’s one of the best Big Data Tools for scaling up from a single server to tens of thousands of commodity computers.
Hadoop is a free and open-source framework for managing distributed Big Data processing across a network of computers. Rather than storing and processing all of the data on a single computer, Hadoop clusters several computers into a virtually indefinitely scalable network and analyses the data in parallel.
This procedure commonly employs the MapReduce programming model, which marshals remote computers to coordinate Big Data processing.
Key Features of Apache Hadoop
- Enables a high level of security, while using HTTP servers.
- Hadoop has POSIX-style filesystem compatibility and authorization, as well as a complete set of properties.
- It comprises a diverse set of Big Data Tools and Technologies that work together to create a robust ecosystem that meets the developer’s Analytical needs.
- Faster and simple Data Processing due to distributed processing.
3) Apache Spark
Apache Spark is a free and open-source software solution for distributed processing. It connects numerous computers and allows them to process Big Data in parallel, which speeds up and simplifies Big Data operations. Spark is gaining popularity due to its use of machine learning and other technologies, which increase its speed and efficiency.
Spark comes with a collection of tools that may be used for a variety of features, including structured data and graph data processing, Spark Streaming, and Machine Learning analysis, in addition to advanced APIs in Scala, Python, Java, and R.
Key Features of Apache Spark
- In Hadoop clusters, Spark applications can run up to 100 times faster in memory and 10 times quicker on storage.
- Specially developed to deal with real-time Data Streaming.
- Over 80 high-level operators are built-in.
- SQL queries, streaming data, and sophisticated Analytics, including Machine Learning and graph algorithms, are all supported.
- It can run in cluster mode independently, as well as on Hadoop YARN, Apache Mesos, Kubernetes, and Cloud.
4) Apache Kafka
Apache Kafka is a distributed event processing or streaming platform that allows applications to process large amounts of data quickly. It is capable of handling billions of events every day. It’s a scalable streaming platform with excellent fault tolerance.
The streaming process entails posting and subscribing to streams of records in the same way as messaging systems do, archiving these records, and then analyzing them.
Key Features of Apache Kafka
- It scales easily and there’s no risk of downtime.
- Can effortlessly work with large volumes of data streams.
- The Kafka cluster can withstand master and database failures.
- Kafka offers high throughput for both publishing and subscribing to messages.
- The Kafka cluster is designed to be fault-tolerant.
5) Apache Storm
Apache Storm is another open-source Big Data Analytics platform that can handle unbounded data streams. Not only is it a fault-tolerant, real-time processing system compatible with all programming languages, but it also supports JSON-based protocols.
Apache Storm is extremely scalable and user-friendly, despite its tremendous processing rates and intricacy.
Key Features of Apache Storm
- It can handle 1 million 100-byte messages per second per node.
- Uses a cluster of devices to do parallel calculations.
- In the event of a node failure, the system automatically resumes and transfers work to another node.
- Each data unit is processed at least once.
- Big Data Analytics Tool with a simple interface.
6) Apache Cassandra
Apache Cassandra is a non-relational database (NoSQL) that offers continuous availability, massive scale, and data dispersion across different data centers and Cloud availability zones. Simply said, Cassandra is a highly dependable data storage engine for applications that need to expand massively.
The open-source version of the Cassandra database is well recognized to have the largest deployment at Apple. Netflix is also a significant user of Apache Cassandra.
Key Features of Apache Cassandra
- Reduce user delay to help with duplication across many databases.
- The capacity to duplicate data to several nodes contributes to high fault tolerance.
- It’s one of the ideal Big Data Tools for firms who can’t afford to lose data, even if an entire data center is down.
- Cassandra shows agreements and perks that are available from third parties.
7) Apache Hive
Apache Hive is a Big Data software solution that is free and open-source. It enables Hadoop programmers to evaluate massive data collections. It makes querying and handling huge datasets easier. It uses HQL (Hive Query Language) to conduct SQL-like queries, which are then transformed to MapReduce tasks internally. You can avoid the need for the usual way of developing complicated MapReduce scripts by using Hive.
Key Features of Apache Hive
- For interface and data modeling, it supports the SQL programming language.
- It assembles a language with two basic functions: map and reducer.
- Hive allows you to program in either Java or Python.
- Only structured data can be handled and queried using Hive.
- Hive is a SQL-inspired programming language that incorporates the complexity of Map Reduce.
- A Java Database Connectivity interface is included as well.
8) Zoho Analytics
Zoho Analytics is a cost-effective and easy-to-use Big Data Analytics solution for small businesses. It includes an easy-to-use user interface that allows you to rapidly construct sophisticated dashboards and identify the most critical data.
Though it’s a good standalone solution, Zoho Analytics has the added benefit of being directly linked with the rest of the Zoho business tools, such as CRM, HR, and Marketing automation.
Key Features of Zoho Analytics
- You can create intelligent dashboards and reports using Zoho Analytics’ drag-and-drop interface.
- It comes with pre-built and easy-to-use analytical functions.
- Provides Software Vendors with an Embedded BI Solution.
- Zoho dashboards make data more accessible to non-IT users.
Cloudera is now one of the quickest and most secure Big Data Tools available. It started as an open-source Apache Hadoop distribution geared for enterprise-class deployments. This flexible platform makes it simple to gather data from any setting.
Cloudera provides various software, support, and service bundles that are offered on-premise and through several Cloud providers.
Key Features of Cloudera
- Provides real-time data monitoring and insights.
- Cloudera Enterprise can be deployed on AWS, Google Cloud, and Microsoft Azure, among other Cloud platforms.
- It is capable of constructing and training data models.
- Data clusters can be spun or terminated. This allows you to just pay for what and when you need it.
- Provides a hybrid Cloud option for businesses.
Another excellent open-source Big Data Analytics Tool is RapidMiner. It can handle data preparation, Machine Learning model development, and model deployment. It comes with a series of add-ons that allow you to construct custom data mining methods and predictive setup analysis.
For small, medium, and large proprietary versions, it provides a variety of licenses. It also appears to have a free version that only allows for 1 logical processor and 10,000 data rows. It’s developed in Java, and it’s quite efficient, even when used in conjunction with APIs and Cloud services. It comes with a set of powerful Data Science tools and algorithms.
Key Features of RapidMiner
- Offers a variety of data management approaches.
- It has a simple graphical user interface (GUI).
- Batch processing is possible.
- Combines databases that already exist.
- It has dashboards that are both interactive and shared.
- RapidMiner can do Predictive Analytics on data.
OpenRefine is one of the powerful tools widely used for Data cleansing and Transforming it into different formats. It works smoothly with large datasets. It is used with extended web services and external data. Its previous name was Google Refine. OpenRefine always keeps data private on your system and also allows you to share it with other team members.
Key Features of OpenRefine
- You can data in multiple formats.
- OpenRefine allows users to explore large datasets within a matter of seconds.
- It handles cells of the table with multiple data values and performs cell transformation.
- You can extend your datasets to various web services.
Kylin is a distributed Data Warehouse and Analytics platform designed for Big Data. It supports very large datasets because it provides an OLAP engine. It is built on the Apache technologies such as Hadoop, Hive, Parquet, and Spark.
Key Features of Kylin
- It provides an ANSI SQL interface for handling multi-dimensional analysis of big data.
- It precalculates the multidimensional OLAP cubes to accelerate the Analytics process.
- It offers easy integration with BI tools such as Power BI, Tableau, etc.
Samza is an open-source distributed stream processing platform that was built by LinkedIn and is now managed by Apache. It allows users to build stateful applications for real-time processing of data from Apache Kafka, HDFS, and other sources.
Key Features of Samza
- Samza has the capability to run itself as an embedded library in Java and Scala applications.
- It offers a fault-tolerant design to deliver quick recovery from system failures.
- Samza comes with inbuilt integrations with other platforms such as Hadoop, Kafka, and various other platforms.
Lumify is an open-source Big Data Analytics tool widely used to analyze and visualize large datasets. It offers a user-friendly interface and allows users to generate insights by drilling down into the data.
Key Features of Lumify
- Lumify comes with a cloud-based environment and integrates well with AWS.
- It is open-source software and a dedicated team backs its development.
- It is scalable and secure to use.
Trino is one of the branches of Presto Query Engine also known as PrestoSQL. Trino allows its users to query data no matter where it is stored by natively running queries in Hadoop and other data repositories.
Key Features of Trino
- Trino is designed for both ad-hoc Analytics and long-running batch queries.
- Trino can gather data from multiple data sources in queries.
- Trino can easily integrate with BI tools such as Tableau, Power BI, etc.
Key Benefits and Use Cases of Big Data Tools
Now that have explored the various Big Data Tools and also understood the need for them, let’s discuss some of the benefits and use cases Big Data Tools and Technologies offer.
1) Effective Risk Management
For many corporate executives, the COVID-19 outbreak was a wake-up call, as they realized how vulnerable their operations were to disruptions. Hence, companies have started to use Big Data insights to predict risk and prepare for the unexpected.
Use Case: Banco de Oro, a Philippine financial firm, is using Big Data Analytics to detect fraud and anomalies. It is used by the organization to narrow down a list of suspects or root causes of concerns.
2) Product Development and Innovations
When companies use powerful Big Data Analytics Tools across all of their operations, they can not only identify inefficiencies but also execute quick and efficient alternatives.
Use Case: Rolls-Royce, one of the world’s major producers of jet engines for airlines and military forces, is using Big Data Analytics to assess the efficiency of engine designs and determine whether any upgrades are required.
3) Faster and Better Decision Making Within Organizations
Big Data Tools enable Product Developers to swiftly monitor and respond to unstructured data such as customer feedback and cultural trends.
Use Case: Starbucks uses Big Data Analytics to make strategic decisions. For example, the corporation uses it to determine whether or not a specific area is acceptable for a new shop. They’ll look at a variety of elements, including population, demography, geographical accessibility, and more.
4) Improve Customer Experience
“Growing organizations are more actively collecting customer experience data than nongrowth companies,” according to a Gartner survey 2020 of global business executives. Businesses can use Big Data Analysis to improve and customize their clients’ brand experiences.
Use Case: To improve customer experiences, Delta Air Lines employs Big Data Tools and Analytics. They monitor tweets to learn about their customers’ trips, delays, and other concerns. The airline monitors negative tweets and takes appropriate action to rectify the problem. It helps the airline create positive customer relations by publicly addressing these concerns and proposing solutions.
In this article, you learned the 5 V’s of Big Data and understood the need for Big Data Tools and Technologies. In addition, you understood the important factors to keep in mind while selecting the right Big Data Tool for your organization. Moreover, this article helped to explore the robust Big Data Tools most popular in the market.
However, extracting complex Big Data from a diverse set of data sources like Databases, CRMs, Project management Tools, Streaming Services, Marketing Platforms can be quite challenging. This is where a simpler alternative like Hevo can save your day!
Hevo Data is a No-Code Data Pipeline that offers a faster way to move data from 150+ Data Sources including 40+ Free Sources, into your Data Warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code.VISIT OUR WEBSITE TO EXPLORE HEVO
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