AWS Redshift is a managed data warehouse solution from Amazon Web Services. It’s part of their popular cloud-based computing platform and used by many familiar enterprises, such as Lyft and McDonald’s. Data warehouses are storage and analytical solutions for large amounts of data. They take data gained via ETL or ELT services like Integrate.io or AWS Glue and turn it into useful information and datasets that businesses can analyze and utilize for strategic insights.

Data. Big data is everywhere in your business and odds are good that you have petabytes of it. From your customer's purchasing information to financing data, you need to make sure that you are properly managing your data. This means working on recording, organizing, and analyzing it. As the old expression goes, "Junk in, junk out." If you don't properly manage your data, you'll have nothing but junk. This means that you have to store your data and datasets.

Note: This is part 2 of the Make the Leap New Year’s Resolution series. For part 1 please go here. When we introduced Cloudera Data Engineering (CDE) in the Public Cloud in 2020 it was a culmination of many years of working alongside companies as they deployed Apache Spark based ETL workloads at scale.

Amazon Redshift is one of the leading big data management services that any business can use to extract, transform and load data for various business uses. Amazon’s AWS platform is designed to help with that by providing access to Amazon Redshift with scalable AWS services. Redshift is complex, which gives you a lot of customization options but can also be harder to optimize without help. Here are 10 Amazon Redshift performance tips to maximize your Amazon Redshift experience.

Amazon Redshift is a fully managed data warehouse solution that allows you to efficiently analyze all your data using your existing business intelligence tools. While Amazon Redshift is one of the industry's top data storage solutions, many considerations need to be made before using AWS Redshift. One of the primary elements of any cloud-based storage solution is knowing how to transfer and secure data properly. Here, we will break down how to properly move data to and from the AWS Redshift platform.

Amazon Redshift, part of Amazon Web Services, is typically the final destination of many data integration projects. Data-driven companies move data from various locations to Amazon's user-friendly, high-performance cloud data warehouse so they can run that data through business intelligence (BI) tools such as Looker and Zoho. This process helps these companies gain unparalleled real-time insights into their organizational workloads so decision-makers can improve workflows.

Microsoft customers can discover, transact and deploy Fivetran’s fully managed and automated data pipelines in Azure and more.

In a digitally powered economy, having access to data can help companies navigate market changes, perform customer analytics and adjust their strategy to meet demand. Unfortunately, most of the data company’s generate are unstructured and siloed across various departments in the organization. According to Forbes, 95 percent of businesses cite the need to manage unstructured data as a problem for their business.

S3 is an excellent way to store files scalably and reliably. This article by Jeff Morhous will show you how to use S3 to store files uploaded to a Rails app.

There has never been a better time to start a startup, thanks to all of the advancements in communications and data management technology. Data engineering and utilization are at the core of every new startup that plans on disrupting and dominating its markets. Fortunately, Amazon Redshift can make the data management aspects of running a business much easier. Here are 11 Amazon Redshift tips for startups.