Big data has been present in the industry for quite some time now. These huge data chunks helps enterprises keep a clear track of the information regarding their customers, products, environment and about themselves.
Working with BDA in Java relies on a number of tools. Most of these are open source, and when used together they form a BDA stack that provides a powerful level of functionality. This article examines some of the top tools.
Previously in the Apache Incubator, Apache ShardingSphere has left its nest and graduated to a Top-Level Project. It is an open source ecosystem that can be used for handling big data and supports different functions including database orchestration. Let’s take a closer look.
We’re going to need high dimensional probability and statistics and model it in high dimensional geometry. This is why AIOps is inevitable. This article examines four of the reasons why people distrust AI and what properties define big data.
In the bigger picture, big data services companies are providing better-integrated Data analytics services and an AI scale to accelerate innovation. What is unified data analytics? Read about the Unified Data Analytics platform and the benefits of AI and big data analytics solutions.
The Apache Kafka open source platform is particularly popular in the context of big data applications that require the processing of many data streams. Version 2.4 of the stream processing software has now been released – consumers can now fetch from closest replica and there is a new Java authorizer API.
The Apache Software Foundation recently announced that Apache Rya has moved on up to become a Top Level project. What is Apache Rya? This scalable RDF big data management system is used by a number of organizations, including U.S. Department of Defense agencies for advanced tactical communications. Find out more, including how you can contribute and join the community.
Data lakes are just one way to organize and structure big data, and is one of the most relaxed in terms of data preparation and organization. As we are on the brink of a paradigm change fueled by the explosion of available information, here are the top reasons to look at data lakes as a data management option. Let’s take a look.
Reinforcement learning learns complex processes autonomously. No big data sets with the “right” answers are needed; the algorithms learn by experimenting. By using reinforcement learning, robots learn to walk, beat the world champion in Go, or fly a helicopter.
Migrating to the cloud isn’t the easiest task however, you can limit its complexity. Smooth out the plan for migrating big data to the cloud with a step by step plan. Learn the correct questions to ask yourself before migrating big data workloads to Azure HDInsights in order to ensure a perfect, error-free migration.
How does the rise in mobile app development affect big data? The amount of data continues to grow at unprecedented levels. As the total data produced will cross zettabyte level in a few years, big data analytics is required to be in the picture for high-level analytics and deriving valuable insights from the vast pool of data.
What are the challenges of big data? How can organizations use its benefits to generate ROI? Vaishnavi Agrawal gives an overview of everything big data – from customer relationship management to fraud detection and cost reduction.
Many organisations have diverted to new trends and technologies in order to aid faster decision-making amid a rapidly transforming business environment, and one area in which this is particularly palpable is Big Data. With this in mind, we’ve spoken to two industry experts to get the skinny on why leveraging Big Data should be the main priority for businesses of all sizes in the next twelve months, as well as how best to implement a strategy that incorporates the concept in 2019 and beyond.
Is your company’s algorithm completely optimized? It’s more important than ever to remain on top of the equations that keep the internet running. In this article, Christophe Thibault explains how enterprises should invest in big data algorithm officers for both product development and commercial strategies.
