Big data analytics

Big data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.. See complete definition.

January 06, 2021 06 Jan'21
DataOps engineer an emerging role in analytics

As organizations accumulate more data and realize the need to implement a DataOps strategy, they are now seeking DataOps specialists to help implement and oversee data management.
December 08, 2020 08 Dec'20
SingleStore raises $80M for distributed SQL database

Raj Verma, CEO of SingleStore, explains why the vendor rebranded from MemSQL and how its platform is more than just an in-memory database, but is a general-purpose database.
December 04, 2020 04 Dec'20
The top 5 enterprise advanced analytics use cases

Advanced analytics adoption has been growing across industries. The technology has many growing use cases in enterprises, but these are the top five.
December 04, 2020 04 Dec'20
Collibra grows enterprise data governance for the cloud

Collibra CEO discusses the importance of data governance for enterprises and how to tie data governance to business terminology to go beyond simply controlling data.

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Computers don't care about the style of your code, so why should you? See what Al Sweigart has to say about code formatting, and get a sneak peek at his new book. Continue Reading
big data analytics

Big data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions. Continue Reading
Key differences in uses of DataOps vs. DevOps

As with DevOps, DataOps hinges on cooperation between teams and breaking down silos within an organization with the focus of implementing and maintaining a data architecture. Continue Reading

Weigh the pros and cons of technologies, products and projects you are considering.

Top 5 enterprise graph analytics use cases

Gartner expects enterprise graph analytics adoption to grow in the coming years. Read on to find out why the technology is on the rise across organizational markets. Continue Reading
Do you need a data warehouse for business intelligence?

Some organizations have moved away from using data warehouses in their business intelligence strategies. Read on to find out how approaches to data storage for BI are changing. Continue Reading
Decision intelligence software boosted by analytics, AI

Enterprise adoption of decision intelligence is projected to grow in the next few years. Find out how analytics and AI support decision modeling and how organizations are using it. Continue Reading

Learn to apply best practices and optimize your operations.

Why using graph analytics for big data is on the rise

Graph analytics is being used across industries for different reasons. Read on to see how they can improve organizational decision-making, network analysis, production and more. Continue Reading
The data science process: 6 key steps on analytics applications

The data science process includes a set of steps that data scientists take to gather, prepare and analyze data and present the analytics results to business users. Continue Reading
8 types of bias in data analysis and how to avoid them

Analytics can exhibit biases that affect the bottom line or incite social outrage through discrimination. It's important to address those biases before problems arise. Continue Reading

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Overcoming the top 3 analytics adoption challenges

Analytics is a growing part of many businesses. Experts talk about some of the most pressing challenges enterprises may face when implementing new analytics strategies. Continue Reading
COVID-19 data resources for volunteering data scientists

Many data scientist are looking to research the novel coronavirus and ways to fight the continued outbreak. Read on for the top COVID-19 data resources for volunteer researchers. Continue Reading
7 steps to a successful data lake implementation

Flooding a Hadoop cluster with data that isn't well organized and managed can stymie analytics efforts. Take these steps to help make your data lake accessible and usable. Continue Reading