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Short Summary: In this guide, we address performance problems that occur when PostgreSQL queries very large tables containing low-cardinality boolean fields. It also demonstrates how composite indexes, created and tested with Devart tools, allow PostgreSQL to locate the required rows directly instead of scanning the entire table.

Short Summary: This article explains how to expand your CLI query tool so it can handle multiple databases. First, we focus on key relational systems, then move to data warehouses. We’ll use dbForge Edge as a reference point, showing how different databases can be managed in one place while keeping automation simple.

Picking an SQLite ODBC driver is like choosing a key; you need one that opens every door cleanly, without snapping or jamming. The right driver must connect your data effortlessly—no errors, no limitations. It also has to withstand real challenges: complex queries, cross-platform environments, and large datasets. In this guide, we’ll show you which 2026 drivers stand up to these demands, so you can pick the one that won’t let you down.

Short Summary: Counting related records across multiple dependent tables can produce wrong totals or slow queries when standard joins are used. This article explains a more reliable way to do this by counting each table separately, applying date filters, and checking that the query still runs well on large datasets. Tools such as dbForge Edge can also help if needed.

Short Summary: Different databases use different SQL syntax for simple tasks like subtracting days from a date. In this post, we show how these differences appear across databases such as MySQL, PostgreSQL, SQL Server, Oracle, and H2. We also explain how dbForge Edge helps teams work with them in one place while keeping application logic consistent.

The MySQL ODBC driver is what keeps BI tools, reporting systems, and ETL pipelines connected to MySQL without errors. Teams have depended on it for years, and it’s still vital today, especially with MySQL ranked worldwide in February 2026. However, not all ODBC drivers are built alike. There are two categories: open-source options and commercial ones. While both connect applications to MySQL, they differ in areas like stability, performance, security, and support.

Short Summary: This guide shows how to use time-based LEFT OUTER JOINs with SQL Server temporal tables, step by step. You’ll see how dbForge tools help you fine-tune these queries so you can get accurate reports for specific points in time, fully understand how your data changes, and confirm that your logic is correct.

Most database problems get blamed on queries, schemas, or infrastructure, and rarely on SQL Server ODBC drivers. Fair enough, those components often break. But in many cases, the real culprit is the connection layer itself. Poor drivers can lead to security gaps, performance hits, cloud connection struggles, Unicode issues, or incompatibility across platforms and modern SQL Server versions.

PostgreSQL ODBC drivers are no longer background components. For teams running BI, reporting, and ETL on PostgreSQL, the drivers directly affect how fast queries run, how reliably dashboards refresh, and whether data pipelines remain stable as usage grows. As PostgreSQL moves deeper into analytics stacks, these capabilities are driving the demand for these tools, a trend reflected in broader ODBC market growth.

We are excited to announce the release of SSIS components Data Flow Components version 4.0, an update that delivers expanded compatibility with the latest development tools and database platforms. Version 4.0 introduces full support for Visual Studio 2026 and Visual Studio 2026 Insiders, ensuring developers can seamlessly adopt Microsoft’s newest IDE while continuing to work with familiar workflows.