Systems | Development | Analytics | API | Testing

In this episode of Kongcast, Matt Stratton, a staff developer advocate at Pulumi, explains the history of configuration automation, the world of cloud engineering and how it compares to DevOps. Check out the transcript and video from our conversation below, and be sure to subscribe to get email alerts for the latest new episodes. Viktor: So before we jump to this one, tell us a bit about yourself. Matt: I spent about two decades working in traditional technology operations. I was a sysadmin.

Amazon uses a lot of adjectives to describe its cloud data warehouse: AWS Redshift is "fast," "simple" and "cost-effective." It's also popular. GE, McDonald's, Bosch, Coca Cola, and countless other brands, ranging from startups to Fortune 500 companies, have added Redshift to their tech stacks. But why is AWS Redshift used? And why is it the world's No.1 cloud data warehouse? Below, learn more about what Redshift does, how it does it, and why it could be a great fit for your organization.

When CTO James Dixon coined the term “data lake” in 2011, he imagined a single storage repository where organizations could store both structured and unstructured data in their raw format until it was needed for analytics. But without the right storage technology, data governance, or analytical tools, the first data lakes quickly became “data swamps” - morasses of data with no organizational structure and no efficient way to access or extract meaningful insights.

Amazon Redshift says it executes data operations ten times faster than other enterprise data warehouses because of a hardware-accelerated cache called Advanced Query Accelerator (AQUAD). It also claims three times better price-performance than other similar technologies. Statements like these are what make Redshift an attractive option for companies that want to push data into a warehouse for analytics.

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.