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.

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.

In the data landscape, the people are represented by two separate yet equally important groups. The data engineers who design the Lego blocks and the data scientists who build something extraordinary out of them. These are their stories. DUN DUN! And we’re back! Last time, we went over the toolkit needed to get your foot in the door as a data engineer. You’ve gotten over the first hurdle, but I hope you haven’t fallen prey to the Dunning-Kruger Effect.

Imagine you’re driving a car. You can see what’s happening on the road in front of you, but you have no idea what’s going on under the hood. It’s like driving blindly without any gauges or a dashboard to give you vital information. You don’t know how fast you’re going, how much fuel you have left, or if something is about to go wrong. In the same way, data engineers who lack data observability are like drivers with a limited view of the road.