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If you have read some of my previous posts, you already know that I am not one to follow conventional wisdom. Like many of my Concurrency colleagues, the idea of following generally accepted theories or beliefs it is not what excites us, and it is certainly not what drives us.

How we create value for our customers is not by following a solution-first approach. But it is more about following a people-first approach.

Let us explore together what I mean by that.

Jaime Velez by Jaime Velez

The Data Lake vs. The Data Warehouse

Both a Data Lake and a Data Warehouse are options for storing data. While traditionally data warehouses have been the preferred storage method of organizations, recent advancements and cloud computing have seen a rise in data lakes. While both storage systems, one is not a replacement of the other, and both have their place in the modern data framework. 

Steve Campbell by Steve Campbell

Executive Lunch in Minneapolis

Concurrency pulled together clients and our sales excutive team for an Executive Briefing at Crave in Minneapolis to discuss a recent client success.  A midwest airline completed a success data project with Concurrency using PowerBI, Azure Data Bricks and Data Lake to load and analyze booking data across their enterprise.  The brief was attended by several other clients who are planning to enbarck on a simular engagement and wanted to learn more about the process.

Nick Rustad by Nick Rustad

Power BI vs SSRS

A detailed description of the differences between Power BI and SSRS

Zed Dietrich by Zed Dietrich

Moving Big data in and around Azure using Azure Data Factory.

There are numerous data storage options available on Azure, each one designed and developed for different modern data storage scenarios. These storage options could be in the form of database, data warehouse, data caches and data lakes. Usage of these depends on the application and the scale that they serve. Within databases, some applications might need relational database, some might need NOSQL, or a key-value storage, or in-memory database (for caching), or blob storage (for media and large files). Another criteria to keep in mind when selecting a database for your application is the required read-write throughput and latency. Azure has a wide array of fully-managed database services which frees up the development teams valuable time in managing, scaling and configuring these databases.

Whatever database you choose, you should also keep in mind how easy or difficult it is to move the data in and out of that database. You might have a situation in future where you need to move to a new database solution because of reasons like change in application architecture, scale, performance, or even cost. Microsoft Azure has a very powerful ETL tool called Azure Data Factory to easily move data in and around Azure at scale. It has over 80 native connectors which can serve both as source and sink.  In this blog, I would like to highlight a few features and concepts of Azure Data Factory which will serve as a quick start guide for anyone looking to do data movement and transformation on Azure.

Siddharth Bhola by Siddharth Bhola

Data Collection on Twitter

Learn Data Collection on Tweeter with python and a Tweeter Developer account.

Alex Zhang by Alex Zhang

What is the European Union’s GDPR, and why is it important?

With less than a year before the General Data Protection Regulation takes effect, we’re kicking off a series of blog posts to get everyone up-to-speed with the changes. The GDPR is a new European privacy law that will require companies, government agencies, non-profits and other organizations that offer goods and services to people in the European Union, or analyze data tied to EU residents, to make some pretty big policy changes.

Concurrency Blog by Concurrency Blog