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Data Warehouses

Databases vs. Data Warehouses: What are the differences?

When it comes to data management, there are two main options: databases and data warehouses. Both have their unique benefits and drawbacks, so it can be challenging to decide which option is best for your business. This article will discuss the differences between databases and data warehouses and help you decide which option is right for you.

ThoughtSpot supports Amazon Redshift Serverless

As companies go all in on the cloud to dominate the decade of data, agility, flexibility, and ease of use are critical to success. That’s why we’re so excited to announce ThoughtSpot’s support for Amazon Redshift Serverless which allows customers to leverage the Modern Analytics Cloud to run and scale analytics on Amazon Redshift without having to provision and manage any data warehouse infrastructure.

Support Multiple Data Modeling Approaches with Snowflake

Since I joined Snowflake, I have been asked multiple times what data warehouse modeling approach Snowflake best supports. Well, the cool thing is that Snowflake supports multiple data modeling approaches equally. Turns out we have a few customers who have existing data warehouses built using a particular approach known as the Data Vault modeling approach, and they have decided to move into Snowflake. So the conversation often goes like this.

Data Lake vs Data Warehouse: 7 Critical Differences

Here are seven key differences between data lakes vs data warehouses: A lot of terms get thrown around in the big data space that every business should understand. Many of these terms are easily confused with each other. This is the case with data lakes vs data warehouses. What are some of the most important differences between them, and how can your business use them most effectively for data analytics and data management? Read on to learn the differences between data lakes and data warehouses.

Data Warehouse Automation: What, Why, and How?

Building a data warehouse is an expensive affair and it often takes months to build one from scratch. There is also a constant struggle to keep up with the large volumes of data that is constantly generated. On top of that, setting up a strong architectural foundation, working on repetitive and mundane data validation tasks and ensuring data accuracy is another challenge. This puts tremendous stress on data teams and data warehouses. Data warehouse automation is intended to handle this growing complexity.

Data Warehouse Automation: What, Why, and How?

Data Warehouse Automation helps IT teams deliver better and faster results by getting rid of repetitive design, development, deployment and operational tasks within the data warehouse lifecycle. With automation, organizations can accelerate the data to the analytics journey, work more effectively with large amounts of data and save cost. Join this session with Darshan Wakchaure, Global Data & Analytics Competency Head, Tech Mahindra as he shares his insights on the key benefits of Data Warehouse Optimization and how to achieve Data Warehouse Automation at scale.

Manage Your Amazon Redshift Data With Integrate.io

Amazon Redshift is one of the world’s most popular cloud data warehousing solutions. Along with other Amazon Web Services offerings, such as Amazon S3 and Amazon EMR (Elastic MapReduce), Redshift can help enhance your data workflows and manage your enterprise data. With the right expertise, Redshift can help you integrate all of your data sources, from operational databases and data lakes to third-party files and websites.

Memory Optimizations for Analytic Queries in Cloudera Data Warehouse

Apache Impala is used today by over 1,000 customers to power their analytics in on premise as well as cloud-based deployments. Large user communities of analysts and developers benefit from Impala’s fast query execution, helping them get their work done more effectively. For these users performance and concurrency are always top of mind.