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What: Sisense at AWS re:Invent Date: December 2-6, 2024 Where: Las Vegas, NV Booth: 225
Discover how AI data analytics tools assist data engineers in seven key ways, including automating routine tasks and supercharging data prep and ETL processes.
Explore top AI machine learning strategies for analytics success. From predictive modeling to explainable AI, unlock key insights for your projects.
The retail analytics industry is constantly evolving, meaning there is a consistent swathe of data that’s being collected every single day – from emerging trends and sales, to changes in the global market and everything in between. Gathering, measuring, and…
A brief tutorial Group by is one of the most frequently used SQL clauses. It allows you to collapse a field into its distinct values. This clause is most often used with aggregations to show one value per grouped field…
Here’s a compelling question: Do we make rational or emotional investment decisions? You may consider yourself as someone who carefully considers every financial decision, but when it comes to selling and buying dollars and euros, we have found some behavioral…
The SQL join operation is one of the most powerful and commonly used SQL operations, but little attention is paid to how the internal SQL engine breaks down the tasks of join operations. This post will explore the common algorithms…
String parsing is a common task for data analysts and data engineers working with raw data. With the growth of unstructured qualitative data, parsing strings efficiently has become increasingly important for fast analysis. One common use of string parsing is…
Inevitably, the unexpected happens. A historically low-traffic channel brings in 10x the normal amount of users. Or your user login rate drops by half. In either case, these are important events that are easy to miss in a sea of…
Views are virtual tables that can be a great way to optimize your database experience. Not only are views good for defining a table without using extra storage, but they also accelerate data analysis and can provide your data extra…
Sometimes it’s helpful to look at an aggregated overview of many rows. With numeric columns, it’s easy to sum or average many values, but for string columns we need something different. We can concatenate strings from multiple rows with concatenating…
Many SQL implementations don’t have loops, making some kinds of analysis very difficult. Postgres, SQL Server, and several others have the next best thing — recursive CTEs! We’ll use them to solve the Traveling Salesman Problem and find the shortest…
There are a lot of decisions to make when creating new tables and data warehouses. Some that seem inconsequential at the time end up causing you and your users pain for the life of the database. We’ve worked with thousands…
Keeping track of your row counts can be helpful for budgeting and capacity planning, yet accurate counts are surprisingly hard to get. Databases keep row counts in their stats tables for planning query execution, but the results are only approximate…
Previously we discussed how to write rolling averages in Postgres. By popular demand, we’re showing you how to do the same in MySQL and SQL Server. We’ll cover how to annotate noisy charts like this: With a 7-day preceding average…