Processing Unstructured Data 101 May 11, 2022 by Mark Smallcombe Unstructured data does not have a predefined schema. How can your organization process this?
Hadoop vs. Redshift: What You Need to Know May 06, 2022 by Mark Smallcombe Nearly a decade later, the Hadoop vs. Redshift debate continues. If you're seeking a reliable data warehouse solution, here's how to choose.
Storing Apache Hadoop Data on the Cloud - HDFS vs. S3 January 11, 2022 by Abe Dearmer Ken and Ryu are both the best of friends and the greatest of rivals in the Street Fighter game series. When it comes to Hadoop data storage on the cloud...
Behind "Amazon Redshift is 10x faster and cheaper than Hadoop + Hive" slides December 07, 2021 by Abe Dearmer We been providing a web service to make it easy to process & analyze big data on the cloud using Hadoop, utilizing Amazon Elastic MapReduce.
Redshift vs Hadoop & Hadoop Hive: A Brief Comparison December 03, 2021 by Donal Tobin We examine the history and capabilities of Redshift and Hadoop, and how they compare across price, performance, and ease of use.
7 Tips to Improve ETL Performance September 29, 2021 by Integrate.io There are several ways you can increase your ETL speed - concentrate on bottlenecks, load data incrementally, partition large tables, process only relevant data, try caching, and use parallel processing.
Build a Data Pipeline with Heroku ETL & Hadoop July 15, 2021 by Abe Dearmer Learn why you should build an ETL (extract, transform and load) pipeline using Integrate.io with Heroku ETL and Hadoop.
Getting to Know the Apache Hadoop Technology Stack June 24, 2021 by Mark Smallcombe Understanding the Apache Hadoop tech stack can help you determine if this technology is the right solution for you. Learn all about it here.
MongoDB + Integrate.io = Perfect Big Data Stack February 01, 2021 by Integrate.io Everything you need to know about MongoDB Hadoop integration in your data stack. And why you should do it with Integrate.io.