Tuesday, June 26, 2018

Fun With SQL: Functions in Postgres

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Fun With SQL: Functions in Postgres
Fun With SQL: Functions in Postgres

In our previous Fun with SQL post on the Citus Data blog, we covered window functions. Window functions are a special class of function that allow you to grab values across rows and then perform some logic. By jumping ahead to window functions, we missed so many of the other handy functions that exist within Postgres natively. There are, in fact, several hundred built-in functions, and when needed, you can also create your own user defined functions (UDFs) if you need something custom. Today, we’re going to walk through just a small sampling of SQL functions that can be extremely handy in PostgreSQL.

Arrays

First, arrays are a first-class datatype within Postgres. You can have an array of text or an array of numbers. Personally, I love using arrays when dealing with category tags. You can also index arrays, which can make querying extremely fast, but even if you’re not putting arrays directly into your database, you may want to build up arrays within your query.

What’s New with AWS – Week of June 18, 2018

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#Data -What’s New with AWS – Week of June 18, 2018

Chunk Change: InnoDB Buffer Pool Resizing

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Chunk Change: InnoDB Buffer Pool Resizing
Chunk Change: InnoDB Buffer Pool Resizing

Since MySQL 5.7.5, we have been able to resize dynamically the InnoDB Buffer Pool. This new feature also introduced a new variable — innodb_buffer_pool_chunk_size — which defines the chunk size by which the buffer pool is enlarged or reduced. This variable is not dynamic, and if it is incorrectly configured, it could lead to undesired situations.

Let's see first how innodb_buffer_pool_size, innodb_buffer_pool_instances, and innodb_buffer_pool_chunk_size interact:

Power BI Sudoku, Custom fonts, DAX and more... (June 25, 2018)

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#Data -Power BI Sudoku, Custom fonts, DAX and more... (June 25, 2018)

Monday, June 25, 2018

Traditional Database Security Doesn’t Protect Data

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Traditional Database Security Doesn’t Protect Data
Traditional Database Security Doesn’t Protect Data

It seems every week there’s a new data breach to read (or tweet) about. I recently discovered this lovely visualization of the growing amount of private data about people like you and me that is being exposed. You can filter and/or sort the data by industry sector, method of leak, and data sensitivity. It makes for a beautifully depressing coffee break.

After reading that, you might like to check to see if your details have been included in any of the data breaches listed on haveibeenpwned.com. Thanks to this site, and an alert I received from it following the 2016 LinkedIn breach, I now use a password manager — and I recommend you do the same.

Sunday, June 24, 2018

Are You a Data Professional? It Pays to Stay Home!

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Are You a Data Professional? It Pays to Stay Home!
Are You a Data Professional? It Pays to Stay Home!
2018 Data Professionals Salary Survey Results

Earlier this year, Brent Ozar completed his 2018 Data Professionals Salary Survey and published the results:

I played with the results last year in my 2017 post titled, "When Does It Pay for a DBA to Have an Associate Degree?" — which was a fun post to write. This year, much of the Twitter conversation focused on a gender gap in the results (see "Female DBAs Make Less Money. Why?").

Saturday, June 23, 2018

AWS Summit Mexico City 2018 - Caso de Éxito Kueski [Spanish]

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#Data -AWS Summit Mexico City 2018 - Caso de Éxito Kueski [Spanish]

Codex KV: Properly Generating the File

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Codex KV: Properly Generating the File
Codex KV: Properly Generating the File

The previous post has a code sample in it that was figuratively physically painful for me to write. Avoiding the number of syscalls that are invoked, the code isn’t all too efficient as I now measure things. It uses way too much managed memory and it is subject to failures as we increase the amount of data we push through. For this post, I’m going to be rewriting the CodexWriter class as I would for code that is going into RavenDB.

I’m sorry, there is going to be a big jump in the complexity of the code because I’m going to try to handle performance, parallelism, and resource utilization all at once. The first thing to do is to go into the project’s settings and enable both unsafe code (without which is it nearly impossible to write high-performance code) and C# 7.3 features, we’ll need these.

Amazon Neptune: Build Applications for Highly Connected Datasets

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#Data -Amazon Neptune: Build Applications for Highly Connected Datasets

Friday, June 22, 2018

MongoDB Ruby Driver 2.5.x Case-Sensitivity Issues With Hostnames on Replica Sets

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MongoDB Ruby Driver 2.5.x Case-Sensitivity Issues With Hostnames on Replica Sets
MongoDB Ruby Driver 2.5.x Case-Sensitivity Issues With Hostnames on Replica Sets

Having trouble connecting to MongoDB replica sets after upgrading the MongoDB Ruby driver to 2.5.x? We've recently received a few inquiries about this issue with the latest MongoDB Ruby driver version and wrote this post to share our findings on the problem and cause.

The error message that was encountered on connection attempt was:

Thursday, June 21, 2018

How Realm is Better Compared To SQLite

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How Realm is Better Compared To SQLite
How Realm is Better Compared To SQLite

While starting a new application, we often wonder which database to use, especially if the application is database intensive. Recently, I came across Realm, which is really well-built and surprisingly very fast compared to SQLite. In this post, I aim at showing how Realm compares to SQLite.

Let’s start with looking at basic CRUD operations in Realm.

Introducing Amazon EKS

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#Data -Introducing Amazon EKS

Wednesday, June 20, 2018

Castilleja School Automates Data Protection and Shortens RTOs

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#Data -Castilleja School Automates Data Protection and Shortens RTOs

Getting Started with MongoDB #3

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Getting Started with MongoDB #3
Getting Started with MongoDB #3

Hello everyone! In my previous article, I explained CRUD operations in MongoDB, which you can find here. In this article, I will explain some leftover parts like sorting, projection, comparison query operator, logical query operator, and many more.

Before starting, let's insert a document first:

Centralized and Externalized Logging Architecture for Modern Rack Scale Applications using NVMe Shared Storage

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Centralized and Externalized Logging Architecture for Modern Rack Scale Applications using NVMe Shared Storage
Centralized and Externalized Logging Architecture for Modern Rack Scale Applications using NVMe Shared Storage

“We are a log Management Company that happens to Stream Videos”

-Netflix Chief Architect

Fun With SQL: Functions in Postgres

DZone Database Zone Fun With SQL: Functions in Postgres In our previous  Fun with SQL  post on the  Citus Data  blog, we covered w...