Systems | Development | Analytics | API | Testing

Amazon Redshift is a completely managed Data Warehouse, offered as a cloud service by Amazon. It can scale up to petabytes of data and offers great performance even for complex queries. Table of Contents Its columnar nature with Postgres as the querying standard makes it very popular for analytical and reporting use cases. These days it is very common to use Amazon Redshift as the backbone Data Warehouse for highly reliable ETL or ELT systems.

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.

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).

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.

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.

Data warehousing is the process of collating data from multiple sources in an organization and store it in one place for further analysis, reporting and business decision making. Typically, organizations will have a transactional database that contains information on all day to day activities. Organizations will also have other data sources – third party or internal operations related. Data from all these sources are collated and stored in a data warehouse through an ELT or ETL process.