Big Data Technologies: List, Stack, And Ecosystem In Demand

Over the last decade, a whole new landscape of various big data technologies and tools has arisen to help companies analyze the market and influence customers.

Successful businesses pick the best big data technology stack and combine it with advanced skills to create business strategies and predict the future.

On this page:

  • What is big data technology? Definition and overview.
  • What is a big data ecosystem?
  • A list of the top big data technologies and tools in demand.
  • How to choose the best big data technology stack for your needs?
  • Infographics in PDF.

What Is Big Data Technology? And What Is Big Data Ecosystem?

The term “big data” relates to data that has a massive volume.

Big data technology is a combination of techniques, tools, and knowledge for collecting and analyzing data.

The purpose is to gain insights that lead to better decisions and improved business results.

How do big data technologies work?

Via special data analytics software tools.

First, data is collected from various data sources such as text documents, emails, images, video, mobile text messaging, social media comments, etc.

Then, the special software tools store, analyze, and visualize data in an easy to understand way, so you can spot patterns and trends in data and come up with valuable conclusions and decisions.

All of these (tools and data sources) are components of the big data ecosystem (or landscape).

A simple example of a big data ecosystem in terms of software tools and data sources is seen below.

Big Data Ecosystem

Top Big Data Technologies And Tools

Let’s break down the above infographic.

Big Data Sources

Each future-oriented company uses various data sources and ways to collect data.

There are two main types of data sources:

Internal and External Data Sources - an Infographic

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  • Internal sources of data: information gathered within the company (such as data from CRM system, ERP system, sales reports, business documents, customer orders, etc).
  • External sources of data: the data collected outside the organization (i.e. government statistics, media channels, regional economic reports, competitive intelligence tools, etc.)

Not all this data is created in equal form, which reflects the way data is collected and analyzed.

Data generated from social media apps are in a completely different format from data generated by a CRM system. The first data is unstructured data, the second – structured data.

So, depending on its format data is classified into two categories:

Structured vs unstructured data - an infographic

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  • Structured data – a highly-organized data that is easily searchable in relational databases. It’s easy to analize and sit in spreadsheets, rows, and tables. Examples: customer’s phone numbers, names, zip codes, Point-of-sale (POS) transactional information. Typical applications that use structured data in relational databases include ERP, CRM, and POS systems.
  • Unstructured data – information that is not organized in some type of data structure, making it much more difficult to collect and analyze. It doesn’t fit nicely into a spreadsheet, tables or databases. Usually, unstructured data is in a text form but can be in a non-textual format as well. Examples: emails, audio and video files, images, Word docs, PowerPoint presentations, social networking data from sites like Facebook, Twitter, and LinkedIn, etc.

Structured and unstructured data live in completely different databases.

Almost anyone familiar with big data technologies knows that only 20 % of the data available to businesses is structured data, and 80 % is unstructured.

Yet both types of data have crucial importance in effective data analysis.

Big Data Storage Technologies

Big data storage tools are designed specifically to store, manage and retrieve massive amounts of data. They enable the sorting of big data in easy-to-access and process way by different applications.

They also support input/output operations on storage with a huge number of data files and objects.

Big data storage software can flexibly scale as required.

Let’s see the top big data technologies used to store a vast amount of structured and unstructured data.

1. Apache Hadoop

Apache Hadoop is like a rock star in the big data storage. It provides an ecosystem, framework, and technology designed for the collection, storage, and analysis of vast amounts of data sets.

Apache Hadoop is a highly-scalable solution. It can automatically scale up and distributes large data sets across clusters of servers or thousands of machines.

If errors happen within a cluster or machine, Apache Hadoop can instantly find them and give you methods to restore and repair the failures.

2. MongoDB

MongoDB is a document-oriented NoSQL database as a service (DaaS). It can be easily accessed and scaled in the cloud and thus allow businesses to run it anywhere.

Companies of all sizes use MongoDB for developing modern applications and gaining competitive advantages as it combines the benefits of relational databases and innovations of NoSQL.

MongoDB allows for faster and iterative development. It provides a flexible data model, dynamic schema, powerful GUI and stores data in JSON-like documents.

3. Cassandra

Cassandra is a database that can handle massive amounts of unstructured data. If your database needs to quickly scale with a little growth in administrative tasks, Cassandra is a great option.

Cassandra can handle so big applications as Instagram that have millions of photos uploaded daily.

Cassandra is in use at eBay, GitHub, GoDaddy, Instagram, Intuit, Netflix, Reddit, and over 1500 more companies that have large, active data sets.

4. MarkLogic

MarkLogic is an enterprise NoSQL database technology – one of the best databases for integrating data from silos.

It is a fast cloud-based platform that helps in data management and does not require data modeling.

MarkLogic is a secure, agile, and scalable platform used by large organizations from Fortune 500 as well as small and medium-sized businesses to run critical business operations.

5. Riak KV

Riak KV is a flexible NoSQL database, highly optimized for IoT and time-series data. It is highly available, scalable and easy to operate.

Riak is very powerful in handling large volumes of requests. It performs great under large volume data and keeps the response time low.

Riak KV ensures fast performance and fault-tolerance via the automatic distribution of data across the cluster. It also allows storage of many data types. You can store various JSON documents in a single bucket.

Big Data Mining Technologies

Data mining tools allow you to extract usable and important data from a huge set of raw data to find relationships, patterns, and anomalies.

To mine data, the tools use techniques such as statistical analysis, special algorithms, machine learning, artificial intelligence, etc.

The business goal is to find useful information such as patterns and trends in customer behavior and use it to develop more effective marketing strategies and increase sales.

Here are some of the best big data technologies for data mining.

6. RapidMiner

RapidMiner is an open-source data science and machine learning platform that enables you to create, deliver, and maintain predictive analytics.

RapidMiner has a robust integrated suite of tools, features and a user-friendly interface that allows you to design and deploy analytics processes, visual presentations, and models.

It not only helps you understand and analyze your data but enables you to create plans and extract critical statistics for further data-driven decision making.

7. IBM SPSS Modeler

If you are searching for a whole suite of tools dedicated to data mining, IBM SPSS Modeler can be your answer.

IBM SPSS Modeler is a software suite for data and text mining to help you build predictive models.

You can rely on IBM for data preparation and discovery, predictive analytics, text analytics, entity analytics, machine learning, model management, and deployment.

Moreover, SPSS Modeler provides a visual interface that allows you to use data mining algorithms without programming skills.

8. SAS Enterprise Miner

SAS Enterprise Miner is software that streamlines the data mining process to help you develop models quickly and discover valuable data relationships, patterns, and trends.

It has a clean and simplified user interface and is perhaps the best software for developing and evaluating ensemble models.

You also can compare models without writing code and the system suggests which one performs well with the given dataset.

Not only SAS Enterprise Miner provides data mining but it helps identify outliers in the data and remove them quickly.

9. Weka

Weka is a free machine learning software developed at the University of Waikato in New Zealand. It is best suited for data mining and predictive modeling.

Weka is written in Java and has a GUI that ensures easy access to the features.

With Weka, you can do a variety of big data mining tasks like mining, processing, regression, visualization, classification, clustering, and association rules.

It is used for teaching, research, and industrial applications all over the world.

10. Oracle Data Mining (ODM)

ODM is a component of the Oracle Advanced Analytics Database Option and offers data mining algorithms for data classification, prediction, and regression.

ODM helps businesses predict customer behavior, identify cross-selling opportunities, detect anomalies and potential fraud.

Big Data Technologies For Data Analytics

Although data mining technologies incorporate data analysis, there are software tools designed specifically with advanced and powerful analytical capabilities.

They clean, transform, and analyze data, so you can draw insights from big data sets and thus answer business questions and improve results.

They focus on delivering insights to business users in the most timely manner possible.

Let’s see some of the best big data technologies for data analysis.

11. Apache Spark

Apache Spark is one of the most popular open-source big data analytics tools preferred by many organizations that need to deal with large data sets.

It is an easy-to-use, developer-friendly, and incredibly fast engine that achieves high performance for both batch and streaming data.

Spark has over 80 high-level operators that make it a breeze building parallel apps. And you can use it interactively from the Python, R, Scala, and SQL shells.

12. Apache Kafka

Apache Kafka is an open-source stream processing platform written in Scala and Java and provides a very fast and scalable messaging system.

It is absolutely easy to set up according to your needs and requirements.

It is packed with a wide range of big data technologies stack and provides a central broker that governs the master instance and takes care of failure and scaling.

Kafka is widely used for building real-time data pipelines and streaming apps.

13. IBM Analytics

IBM Business Analytics is a great suite of business analytics tools that help businesses increase efficiency in data management in all operations.

It provides automated planning processes and supports you in identifying blind spots that could compromise your analytics.

Moreover, the platform allows you to perform root cause analysis of patterns, relationships, and underlying factors in your data.

The IBM Analytics Engine is great for cases in which you are required to analyze data from a variety of data collection methods and sources.

14. SAP BusinessObjects 

SAP BusinessObjects is a flexible and scalable suite of business analytics tools that allow you to transform data into critical insights and thus understand customer behavior and evaluate business performance.

Also, the software is able to collect data from a very wide range of data sources, so you can gain more accurate insights.

SAP BusinessObjects is equipped with powerful business intelligence querying, analyzing, and reporting tools and allows massive distribution of information automatically.

15. Sisense

Sisense has advanced data analytics tools that allow not only those with data scientist skills to make data analysis but all business users as well.

It makes business data analysis easy, even to non-tech users. Insights are drawn immediately with self-service analytics without coding and modeling.

Some of the best features are: customizable dashboards, great interactive visualizations, and powerful analytical capabilities.

Dashboards are truly fascinating and allow you to seamlessly filter, explore, and mine data in just a few clicks.

Big Data Technologies For Data Visualization

Data visualization software is also a type of data analytics technology.

However, they are specifically designed to take the raw data and present it via beautiful and easy digestible visuals like graphs, charts, diagrams, and maps.

By using graphs and charts, data visualization tools help you see and understand trends, outliers, and patterns in data.

Here are some of the top big data technologies for visualization.

16. Tableau

Tableau is a popular BI (Business Intelligence) software that takes data analysis and visualization to the next level.

It lets you quickly and easily visualize and share data from the desktop to mobile.

Tableau is a very intuitive tool that’s both easy to use and scale. It effortlessly connects with a wide variety of data sources, that enables you to solve problems and discover insights.

The visualizations are in the form of dashboards and worksheets and can provide a unique data analysis solution for any company.

17. Looker

Looker is a superb data visualization tool that can help you make flexible and timely decisions through in-depth data analysis and advanced insights. It lets you utilize SQL to organize raw unstructured data.

Looker features real-time dashboards with beautiful and clear visuals for a more in-depth analysis.

They connect with Redshift, Snowflake, BigQuery, as well as 50+ supported SQL dialects, so you can connect to multiple databases.

18. Klipfolio

Klipfolio is a business dashboard software for everyone. It lets you bring all your data together using hassle-free connectors for hundreds of data sources, including files, databases, spreadsheets, and web apps.

The tool provides a great interaction directly with your data using a wide range of data visualization types. You can instantly spot trends and understand how your business is performing.

The best parts are the drag-and-drop data visualizations with a great choice of visuals like bar charts, scatter plots, line graphs, etc.

19. Chartio

Chartio is a cloud-based data analytics and visualization platform that helps you create powerful dashboards from your business data.

You can analyze data in real-time with a simple drag-and-drop interface. It also comes with many different types of graphs, so you can find patterns, problems or trends across your entire company.

The platform lets you connect all your organizational data from popular sources like Amazon Redshift, Google BigQuery, and PostgreSQL.

With their visual version of SQL, anyone at your business can query data from almost any source – no coding skills required.

Need more big data technologies?

Here is a list of other popular data analytics and visualization tools

This is just a brief insight into the complex and ever-growing big data ecosystem and landscape.

However, not all big data technologies and tools are a match for all users, teams or businesses. This brings us to the next topic.

How To Choose The Right Big Data Technology Stack For Your Needs?

How To Choose Big Data Technology - a short infographic

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With hundreds of data mining and analytics solutions, picking software is hard, because the vendors offer similar features.

Here are some key tips to choose the best big data technology stack that will allow you to analyze your data and gain the most valuable information for your business.

  • Amount Of Data

The big data tool must be capable of managing the amount of data you operate with on a daily basis.

If you process an enormous amount of data daily, the best choice is to get a high-performance tool.

If your organization works with small data, a free big data solution might be a more suitable choice.

  • Data Experts

Do you have data scientists and data analytics experts in your company?

If your team lacks technical skills, it’s a good idea to purchase big data technologies that can automate the majority of tasks or the entire process.

You can select tools that don’t require any assistance from your IT department.

  • Price

Research carefully all costs associated with the software you are estimating – including subscription plans and hidden fees.

Big data technologies are a long-term investment for any organization.

Different data platforms have different pricing model and you need to understand and compare them before making an investment.

Pick a big data tool that you can test for free, before paying for it.

  • Agility And Scalability

Many big data platforms offer you to start with a small pricing plan and then go to a bigger plan according to your business growth.

These pay-as-you-go plans can give you a competitive advantage with analytics that scale according to your business needs.

  • Support (Community And Commercial)

Aim for a big data tool that offers 24/7 support and easy to understand documentation.

Make sure that your big data technologies vendor provides excellent customer support.

Many free and open-source tools aren’t up to date with the latest trends and commercial tools are a better choice in this case.

  • Reputation And Popularity

What is the reputation of each tool you’re estimating? Are the reviews from users positive or negative? Do the reviewers recommend the tool? Which famous companies use the tool?

If you are choosing an open-source solution – how popular and active is the community behind the technology?

  • Access To Different Data Sources

Modern and advanced big data tools can combine multiple sources of data as well as analyze structured and unstructured data.

The tool should have the capability to collect data from different systems (CRM, ERP system, etc.) and to present them in a single dashboard.

Thus, you will be able to see a complete view of your business performance and situations.

  • User-friendly Interface

A good analytics solution has a user-friendly interface that is seamless to use and capable to support different user types.

Even non-technical users should be able to effortlessly create dashboards and reports.

Involve your teams and end-users in the tool selection. Let them participate in the demos and make sure they understand how to use the tool.

Conclusion

Big data technologies are more than emerging tools that allow businesses to examine a vast amount of data.

They provide the most surefire way for companies to make the right decisions about how to win customers, operate more efficiently, and minimize costs.

Data has become the bulwark of almost any business strategy today.

And developing an effective big data technology stack and ecosystem is becoming available to more organizations than ever before.

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