How to Track Query and User Activity After Migrating from Teradata to Snowflake By Roland Wenzlofsky · DWHPro 🎯 Why This Matters In Teradata, QUERY_BAND has long been a hidden gem — the ability to tag every query or session with metadata like App, Job, or UserGroup. That little string became the key to workload …
Performance degradation caused by uneven workload distribution is one of the oldest and most persistent challenges in parallel data warehouse systems. Both Teradata and Snowflake can experience this imbalance, commonly known as skew. Although the term is shared, the mechanics differ fundamentally: Teradata can suffer from both persistent and runtime skew, whereas Snowflake’s skew occurs …
Snowflake’s physical join execution is predominantly hash-based. In practice you’ll observe hash-join variants with two distributions: If you come from Teradata, the intent will feel familiar: both systems aim to co-locate equal keys before matching. This article explains Snowflake’s strategies, maps them to Teradata’s (including dynamic plan fragments), and shows how to recognize and mitigate …
Introduction to the Teradata AMP Worker Task The Teradata AMP Worker Task or AWT is the heart of the AMP, responsible for executing tasks and ensuring the smooth functioning of the system. AWTs are threads that process incoming tasks in the AMP. Each AMP has a finite pool of AWTs, which is shared among all …
Teradata NOS facilitates querying data in an S3 object store with ease. To attain maximum performance, partitioning external data is crucial for efficient reading. This article outlines the key considerations for optimal efficiency when reading data from the object store. To begin, we must establish S3 access by obtaining an AUTHORIZATION object. In this instance, …
Splitting column content can be a challenging task. Teradata provides multiple methods to achieve this, each with unique pros and cons. In this article, we will examine these techniques in detail. To begin, we will generate a table containing sufficient random data to assess every option’s efficiency and resource utilization. The Teradata “CSVLD” Function This …
Learn about the goals of SQL tuning and how to optimize database performance by reducing resource usage. Skew, IOs, and CPU seconds are key metrics. Discover how to ensure completeness and correctness of Teradata statistics, detect missing and stale statistics, and improve query plans.
Learn about Query Logging with Teradata DBQL Tables, a powerful feature for workload analysis and performance tuning. Configure settings and select which key figures to store and their level of detail. The article covers how to implement and activate DBQL tables, determine which information to collect, and analyze tactical queries.
Introduction This blog post discusses tactical workloads on a Teradata system. Despite Teradata’s implementation of features that support tactical workloads, this workload category remains challenging to manage. Selecting an optimal physical design is essential to meet user expectations for query speed. Designing the Teradata tactical workload on a test environment can be frustrating, especially when …
How to find out if the Teradata Statistics we created for a specific workload are used? Teradata statistics greatly affect SQL query efficiency. We need a reliable method to get this information. Various objects, such as tables and join indexes, can have statistics collected on them. As performance tuners, it is important to confirm their …
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