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Big Data

In the last post I explained how to create a set of Azure Functions that could load data into Snowflake as well as execute Snowflake queries and export the results into your favorite cloud storage solution. In this post I will show how we can use these functions in Azure Data Factory to plug Snowflake […]


If, like me, you are a fan of Azure Data Factory and love Snowflake then you are probably disappointed that there isn’t a native Data Factory connector for Snowflake. While we wait for an official connector from Microsoft we have no alternative but to roll our own. In this blog post I will walk you through […]


When he joined endjin, Technical Fellow Ian sat down with founder Howard for a Q&A session. This was originally published on LinkedIn in 5 parts, but is republished here, in full. Ian talks about his path into computing, some highlights of his career, the evolution of the .NET ecosystem, AI, and the software engineering life.


We’re currently building a Data Governance Platform product that enables UK Financial Services organisations to discover and manage the life-cycle, usage, risk and compliance requirements of data assets across the organisation. Much of the core functionality is delivered using Cosmos DB’s Gremlin API to model data lineage and other relationships best represented by a graph […]


Overflowing with dataflow part 1: An overview

by Carmel Eve

This is the first blog in a series about dataflow. The series focuses on TPL dataflow, but this post gives an overview of dataflow as a whole.

The crucial thing to understand when using dataflow is that the data is in control. In most conventional programming languages, the programmer determines how and when the code will run. In dataflow, it is the data that drives how the program executes. The movement of data controls the flow of the program.


Using Python inside SQL Server

by Ed Freeman

Do you have a bunch of data in SQL Server that you’re using ODBC/JDBC to pull data to work with in Python? Using SQL Server’s Python integration, you can connect to a SQL Server instance within your preferred IDE and perform the computations on the SQL Server Machine. No more clunky data transferring. Operationalizing a Python model/script is as easy as calling a stored procedure. Any application that can speak to SQL Server can invoke the Python code and retrieve the results. Easy! This blog will provide a few, simple examples which make use of this capability to carry out some simple Python commands, so you can get up and running as quickly as possible.


Learn what types of things an apprentice gets up to at endjin a few months after joining. You could be learning about Neural Networks: algorithms which mimic the way biological systems process information. You could be attending Microsoft’s Future Decoded conference, learning about Bots, CosmosDB, IoT and much more. Hopefully, you wouldn’t be in hospital after a ruptured appendix!


How to plan your cloud transformation journey

by Howard van Rooijen

This week I received an email from someone who asked how they could use our free Thought Leadership content to help their organisation move to the cloud. I realised that although we’ve released a lot of content, we’d never talked publicly about the rationale behind them and how they are all interconnected. Our Thought Leadership […]


This post explains how to create a PowerBI report which sources data from two separate Azure SQL Databases. PowerBI offers two data access mechanisms; Import and DirectQuery. DirectQuery provides a range of benefits, the chief of which is that data is automatically refreshed. DirectQuery doesn’t import any data into PowerBI, instead it queries the data […]


Choosing the right cloud platform provider can be a daunting task. Take the big three, AWS, Azure, and Google Cloud Platform; each offer a huge number of products and services, but understanding how they enable your specific needs is not easy. Since most organisations plan to migrate existing applications it is important to understand how […]


In this series, we’re comparing cloud services from AWS, Azure and Google Cloud Platform. A full breakdown and comparison of cloud providers and their services are available in this handy poster. We have assessed services across three typical migration strategies: Lift and shift – the cloud service can support running legacy systems with minimal change […]


In February 2016, I completed my second year of endjin’s three year custom apprenticeship scheme. This blog is a chance for me to reflect on what was learnt over the year – hopefully others will find it useful too. Year one had involved a very steep learning curve as I transitioned from student to graduate […]


Machine Learning – the process is the science

by James Broome

What do machine learning and data science actually mean? This post digs into the detail behind the endjin approach to structured experimentation, arguing that the “science” is really all about following the process, allowing you to iterate to insights quickly when there are no guarantees of success.


We produced a booklet to coincide with our Future Decoded talk “The 100 Year Start-up: Embracing Disruption in Financial Services“, where we examine the challenges and opportunities in the Microsoft Cloud for the Financial Services Industry, covering the following topics: Security, Privacy & Data Sovereignty Data Ingestion, Transformation & Enrichment Big Compute Big Data – […]


This post looks at what machine learning really is (and isn’t), dispelling some of the myths and hype that have emerged as the interest in data science, predictive analytics and machine learning has grown. Without any hard guarantees of success, it argues that machine learning as a discipline is simply trial and error at scale – proving or disproving statistical scenarios through structured experimentation.