Here is a monthly list of my latest findings, things I enjoy or ponder. I look into new tools and releases made by Microsoft, its competitors and others. I collect and share with you informative materials about data, AI, cloud. Last but not least, I add futuristic content that I’ve dug up in different places.

A few weeks ago I spoke at Swedbank Seedtalks on Big Data & Analytics in Riga, where I shared my experience in building a Data Science environment on Microsoft Azure.

I met with professionals interested in workload migration to cloud-based services. Many were interested in reducing the risk of letting someone spin up resources and leaving them running for months before someone finds out. Here are some tricks to help you avoid unnecessary expenses.

Do you work with data in Azure? How do you ensure GDPR compliance? This blog post covers a few privacy pattern implementation examples that you might use in your solutions.

The official Azure documentation often takes a siloed approach and misses out more advanced Big Data / Machine Learning end-to-end scenarios. Here is a collection of awesome blog posts on Azure Databricks, Azure Data Factory, Azure Data Lake and other related topics. Understand the bigger picture, build configurable end-to-end pipelines, automate deployment, set up accesses and permissions.

Contributions welcome. Anything missing? Send in a pull request. Thanks!

A red giant star and a white dwarf star sit at the centre of the nebula, forming a binary “hourglass-shaped” star system. The stars perform, as described by NASA, a “gravitational waltz”.

_{Hubble: The Southern Crab Nebula is named after its resemblance (Image: NASA)}

Gartner’s Hype Cycle for Data Management 2018 included DataOps as an emerging topic. Various data analytics vendors picked up the newly-formed term and started offering prepackaged “DataOps solutions”. That triggered a response from Gartner:

DataOps is a practice, not a technology or tool; you cannot buy it in an application. It’s a cultural change supported by tooling, and many of your existing tools may be adequate to bring in the required levels of automation demanded by DataOps. - Hyping DataOps by Nick Heudecker, Gartner

_{Gartner Hype Cycle for Data Management, 2018}

Prieš savaitę dalinausi savo įžvalgomis apie dirbtinį intelektą ir Azure duomenų analitikos paslaugas Microsoft Lietuva organizuotoje konferencijoje. Kadangi daugiausiai klausimų sulaukiau apie „duomenų ežerą“ (angl. Data Lake), nusprendžiau surašyti savo mintis apie pagrindinius bruožus ir atsakyti į dažniausiai girdimus klausimus apie šias duomenų talpyklas.

_{Photo by Joshua Ness on Unsplash}

Bad Blood by John Carreyrou is a true story about big plans and even bigger lies. The hero of the story is a company called Theranos, a health technology startup, that collected millions of dollars from investors to reinvent blood testing. The technology breakthrough wasn’t as simple as the founders expected and they ended up lying and faking their blood testing results for real patients.