10 Big Data Solutions for Modern Data Analytics

Have you ever heard of big data solutions, the world of data, the age of data, and that data is the new oil?

This all has to do with data-driven companies doing modern data analytics.

With this article, we will introduce you to 10 big data solutions used to leverage the growth of companies.

Check it out now!

What is big data?

Big data is an immense volume of data generated in a great variety and in a very fast way that, because of this, cannot be processed by databases or any traditional technologies.

Inserting a big data project in a company brings benefits, such as:

• process optimization;
• increased productivity;
• increase in growth rate;
• cost reduction;
• smarter decision-making.

In addition to making the day-to-day business safer and more efficient.

What about solutions?

Well, we need them to extract value from the immense data set that is big data.

Explore the 10 big data solutions

Listed below, we present 10 big data solutions for doing modern data analytics that every data team should know about.

1) Predictive analytics

With this technology, you can learn from the past, visualize the present, and predict the future. It helps discover, evaluate, optimize, and deploy predictive models through artificial intelligence and machine learning based on big data sources. With this, it is possible to improve business performance, reduce risks and gain much more competitive advantage.

2) NoSQL Database

Compared to relational databases (RDBMS), NoSQL databases are experiencing exponential growth.

This type of database offers a dynamic schema design, as well as greater potential for customization and more flexibility and scalability, which is much needed when storing big data data.

3) Hadoop Ecosystem

The Hadoop Framework is designed to store and process data on different machines with high speed and low cost. This is possible because this tool uses a simple programming model in a distributed data processing environment.

It's important to remember that businesses have always embraced Hadoop as a big data technology. And it continues to grow, so organizations that are going to start exploring Hadoop now are likely to quickly see its advantages and applications.

4) Stream analytics

Streaming analytics, also known as event stream processing, is the analysis of huge pools of data, constantly moving and updated in real time, through the use of continuous queries, called event streams.

Using stream analytics, you can uncover hidden patterns, correlations, and other insights, as well as get almost immediate answers. With this technology, it is possible to upsell, cross-sell to customers based on what the information presents, among other agile actions.

5) Docker

Docker is a big data solution that simplifies the development, deployment, and execution of container applications. In other words, because it works on several platforms, it makes it possible to manage containers on different operating systems.

Because of its siloed perspective on operating systems, it's the ideal alternative for launching all the apps you need with minimal resource consumption, allowing you to containerize apps, deploy them, scale them, and run them quickly.

6) Kubernetes

Kubernetes is one of the open-source tools for big data developed by Google, which performs container orchestration.

In addition, it gives you the freedom of a platform for automating, deploying, scaling, and running container systems on your own local cluster.

7) Data lake

Data Lake is a repository that stores all data formats, whether structured, unstructured, or semi-structured.

Data can be saved before it is transformed, allowing it to be manipulated and analyzed, from the development of visualization dashboards to the transformation of real-time data for agile application in business.

Companies that use data lakes in their day-to-day operations are able to stay ahead of their competitors, as they can perform various analyses through log files, social media data, and click-streaming.

It's a big data solution that helps modern businesses respond better to opportunities and make everyday decisions faster.

8) Data Integration

For data integration, we need tools that allow data orchestration, such as Apache Hive, Apache Pig, Amazon Elastic Map Reduce (EMR), Hadoop, Couchebase, MongoDB, Apache Spark, etc.

9) Cloud

There are numerous advantages that cloud big data solutions offer. The Internet of Things (IoT), for example, perhaps ranks first among the technologies that take advantage of them the most.

Applications involving IoT require accurate and scalable solutions to manage the large volumes of data exchanged in their development and execution, and nothing beats cloud services for this purpose.

10) Data self-service

Any technology that streamlines data cleansing, preparation, and exploration processes tends to grow exponentially, and data self-service solutions are among them.

Its goal is to empower business teams and decision-makers at all levels to use the available data to do their jobs effectively.

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