Features like prebuilt data models, version control and integrated scheduling make data transformation faster and more powerful.

During QlikWorld ’23 in Las Vegas, we were thrilled to announce the general availability of Data Console in Talend Data Inventory. With data-driven decision making becoming more crucial for organizations, it’s never been more important for users to have access to high quality data.

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. 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.

Analyzing gameplay metrics and log data is an essential part of the gaming industry, as it provides developers and publishers with valuable insights into how players interact with their games. Throughout this article, we will outline how analytics, observability, and reporting can aid you in improving your performance whether you are a games developer or a gaming enthusiast.

Without a doubt, Python stands out as one of the most sought-after and adaptable programming languages across the globe. In fact, some of the largest tech companies on the planet use Python, including Google, Facebook and Amazon. Python has been the go-to programming language for many developers, data scientists and researchers due to its ease of use, readability and robustness. But what exactly can Python do?

To get the most out of data centralization and democratization, treat data assets like products.

The digital revolution has sparked a wave of innovation as companies strive to meet consumers where they spend the most time — on web and mobile devices. To keep up with the demands that digital innovations place upon product markets, businesses are increasingly incorporating analytics into their products.