Trends in Digital Transformation: Our Predictions for 2022

WRITTEN BY Buno Pati December 20, 2021
Big Data

In this article, Infoworks CEO Buno Pati summarizes key industry trends.


Why ingest when you can Onboard Data?

WRITTEN BY Amar Arsikere June 17, 2020
Big Data

Every data analytics project starts with the critical first step of creating and operationalizing healthy data lakes. A unified data lake is created by onboarding multiple data sources. Onboarding a data source is more than ingesting the data once.


Infoworks for Databricks

WRITTEN BY Amar Arsikere February 24, 2020
AI and Machine Learning

Data onboarding is the critical first step in operationalizing your data lake. DataFoundry automates data ingestion as well as the key functionality that must accompany ingestion to establish a complete foundation for analytics.


Big Data Trends: Our Predictions for 2020 PLUS What Happened in 2019

WRITTEN BY Ramesh Menon October 31, 2019
Big Data

In this article, we discuss the largest industry shifts in big data from 2019, and where we see big data trends going as we head into 2020.


Data Analytics Use Cases: Traditional vs. Big Data

WRITTEN BY Amar Arsikere October 3, 2018
Big Data

In the not too a distant future, the business world will be split into two camps - companies which have an agile analytics capability and those companies that get eaten by the first group


Straight from the Infoworks blog, our big data articles archive is a collection of key concepts, industry insights, and news happening in the world of big data.

For companies at the enterprise level, big data is one of the most substantial technological innovations to emerge over the last decade. Big data refers to the volume of both structured and unstructured data. It is so large, traditional software and database techniques make it extremely difficult to process. Another way to define big data pertains to what some call “the five V’s”: velocity, volume, value, variety, and veracity.

Those working with big data employ advanced data analysis methods such as predictive analytics and user behavior analytics. Since big data can help businesses make more intelligent and accurate decisions, leveraging this data in certain ways has the potential to deepen relationships with customers, gain a competitive advantage in the marketplace, perform risk analysis, bolster data safety efforts, and create new revenue streams.

Many experts on data storage estimate that the volume of data generated will increase at an exponential rate over the next few years. Therefore big data’s role and the techniques necessary to extract the most value out of it will become increasingly important.

Because traditional relational databases often struggle to meet the processing demands of big data, organizations often turn to NoSQL databases and Hadoop instead. Some of the common complementary tools include YARN, MapReduce, Apache Spark, HBase, Apache Hive, Kafka, and Pig. More recently, IT and analytics teams are embracing data lake warehousing as a repository for incoming streams of raw data.

Want More Big Data Content?

This blog archive is the best place to find articles that share exclusive insights and all of the crucial aspects you need to know about big data. The Infoworks blog also dives into data ingestion best practicesdata engineering articlesnew announcements from the team at Infoworksdata lake news and DataOps articles. Stay up to date with our blog by subscribing to our email newsletter!

If you would like to learn more about enterprise data operations and orchestration, be sure to check out the Infoworks DataFoundry!