A Fine Slice Of SQL Server
The Big Data in Real World team does a bit of data reshaping:
Let’s say we have a table name employee_multiple_depts and each employee in the table is mapped to 3 departments – dept1, dept2 and dept3.
What we need is to transpose or convert each department into a row for each employee.
Click through to see how you can do it in Hive.
Comments closedAlexander Billington shares a few tools and tips:
Machine learning (ML) model deployment is a critical part of the MLOps lifecycle, and it can be a challenging process. In the previous blog, we explored how Azure Functions can simplify the deployment process. However, there are many other factors to consider when deploying ML models to production environments. In this blog, we’ll delve deeper into some of the essential hints and tips for more robust model deployments. We’ll look at topics such as proper model versioning and packaging, data validation, and performative code optimisations. By implementing these practices, data scientists and ML engineers can ensure their models are deployed efficiently, accurately, and with minimal downtime.
MLflow is definitely a good recommendation, as is Pydantic (which is on my to-learn list…one of these days).
Comments closedMatt Pearson and Luke Davies restore a PostgreSQL database:
I had a conversation with another DBA about interview questions, and one interview topic that came up was using pg_basebackup to restore a database. I had a horrible realisation that I had not done a restore using pg_basebackup in PostgreSQL 15. With modern backup tools, using pg_basebackup is like using a manual screwdriver instead of an electrically-powered screwdriver; it gets the job done, but much more effort is involved.
However, sometimes pg_basebackup is the only tool available.
So, in this blog, we’ll look at PostgreSQL’s recovery options and their implications for restoring. We’ll also look at a simple restore using pg_basebackup when a user fails. I’ll be using a PG 15 database for these tests.
Click through for the process and a demonstration.
Comments closedAaron Bertrand is a fan of this new function:
I have come across a lot of use cases for manufacturing rows on the fly, aside from the common goal of populating a large data set such as a numbers or calendar table. A few favorites include building sample data, pivoting an unknown number of columns, data extrapolation, and filling gaps in date or time ranges.
If you are on SQL Server 2022 or Azure SQL Database, or have been reading up on new features, you’ve likely heard about one of the better T-SQL enhancements: a new built-in function called
GENERATE_SERIES. The syntax is straightforward – it accepts arguments forstartandstop, and an optional argument to indicatestep(in case you want to iterate by more than1, or backwards):
Click through to see how performance for this compares to two methods we’ve used in the past to generate similar results.
Comments closedTariq Rasheed Al-Qaralleh recounts a customer problem:
In some cases, like when customers use Object-relational Mapping tools (ORM) Like Entity framework or LINQ, part of the code at the end will be converted to a TSQL executable statement.
For example, The LINQ with .Where () method will be a TSQL Query with a Where clause :
Query Syntax and Method Syntax in LINQ (C#) | Microsoft Learn
Read on for some of the practical consequences of doing this, including performance issues and possibly even runtime errors.
Tariq gives a couple examples of how to fix the issue, and a third possible fix is to pass in the IN clause as a table-valued parameter and join to that TVP.
Comments closedMark Kromer and Revin Chalil show off an interesting preview feature:
Making it super-easy to create efficient and fast ETL processing the cloud, Azure Data Factory has invested heavily in change data capture features. Today, we are super-excited to announce that Azure Cosmos DB analytics store now supports Change Data Capture (CDC), for Azure Cosmos DB API for NoSQL, and Azure Cosmos DB API for Mongo DB in public preview!
This capability, available in public preview, allows you to efficiently consume a continuous and (inserted, updated, and deleted) data from the analytical store. CDC is seamlessly integrated with Azure Synapse Analytics and Azure Data Factory, a scalable no-code experience for high data volume. As CDC is based on the analytical store, it does not consume provisioned RUs, does not affect the performance of your transactional workloads, provides lower latency, and has lower TCO.
Click through to see how it works.
Comments closed