Microsoft SQL Server is kind of a swiss army knife for most SME needs and workloads. However there are a handful of things that SQL Server will be better at, and there’s a handful of things Snowflake will be better at. Table of Contents Snowflake is great if you have big data needs. It offers scalable computing and limitless size in a traditional SQL and Data Warehouse setting. If you have a relatively small dataset or low concurrency/load then you won’t see the benefits of Snowflake.

Often businesses have a different Database to store transactions (Eg: Amazon Aurora) and another Data Warehouse (Eg. Snowflake) for the company’s Analytical needs. There are 2 prime reasons to move data from your transactional Database to a Warehouse (Eg: Aurora to Snowflake). Table of Contents Firstly, the transaction Database is optimized for fast writes and responses. Running Analytics queries on large data sets with many aggregations and Joins will slow down the Database.

Before we dive deep into MongoDB to Snowflake data migration steps, it is important to understand the unique properties of MongoDB and Snowflake that make a data migration like this both challenging and exciting. Table of Contents NoSQL databases like MongoDB address very specific use cases. Data storage and access patterns will be highly optimized for fast write and retrieval and many other factors like the availability and durability of data.

Data maturity models measure the extent to which organizations have developed their data capabilities. They focus on a couple of dimensions that can include strategy, leadership, culture, people, governance, architecture, processes, and technology. Table of Contents The maturity levels of each of these dimensions may be measured along a continuum of four to six levels.

MariaDB and MySQL are two widely popular relational databases that boast many of the largest enterprises as their clientele. Both MariaDB and MySQL are available in two versions – A community-driven version and an enterprise version. But the distribution of features and development processes in the community and enterprise versions of MySQL and MariaDB differ from each other.

In this blog post, we will be covering extensively how you can move data from Google Sheets to BigQuery. Before we get started, let’s cover some background concepts to help build our understanding.

As we hurtle into a more connected and data-centric future, monitoring the health of our data pipelines and systems is becoming increasingly harder. These days we are managing more data and systems than ever before, and we are monitoring them at a higher scale.

When you use different kinds of databases, there would be a need to migrate data between them frequently. A specific use case that often comes up is the transfer of data from your transactional database to your data warehouse such as transfer/copy data from DynamoDB to Redshift. This article introduces you to AWS DynamoDB and Redshift. It also provides 4 methods (with detailed instructions) that you can use to migrate data from AWS DynamoDB to Redshift.

A good data governance strategy should benefit all users of your organization’s data—not just those with technical responsibility for it. Recent years have seen the increasing importance of data as a strategic asset, as several companies have used it to unlock and create value. Increasingly, companies are turning to data governance programs as a foundational pillar of their data strategy (like data mesh) to improve their data sets’ quality, consistency, usability, and security.

According to the U.S. Small Business Administration’s Office of Advocacy, small businesses account for approximately 99.9% of all businesses. That’s a massive chunk of the U.S. economy. While they are high in number, the issues they face are relatively higher, too. Nearly 35% of small business owners report ‌that they aren’t generating any profits, with inflation being the biggest of their worries.