The Best DataOps Articles of Q1 2019

Every quarter we sift through the media and analyst coverage of DataOps and create a list of the best articles for the most recent period. This quarter there was a considerable uptick in activity by individual bloggers writing about DataOps.

Below is our roundup for Q1 2019. If you missed it, here is the previous roundup from Q4 2018. Please tweet us if we missed your favorite.

DataOps: Advancing Data Management, Data Center Knowledge, January 3, 2019

Looking ahead, there are certain solutions that will gain significant importance with special attention being placed on a more collaborative data management approach — DataOps. This methodology is expected to be a major topic of conversation. DataOps is an evolving approach to agile data management that involves the critical combination of people, processes and technology.

DataOps Pillar: Instrument, Kinaesis, January 4, 2019

‘Have you ever been on a project that has spent 3 months trying to implement a solution that is not possible due to the quality, availability, timeliness of data, or the capacity of your infrastructure?’ An example of a failure at a bank comes to mind. Thankfully this was not my project, but it serves as a reminder when I follow the DataOps methodology that there is good reason for the discipline it brings.

For Government to be Truly Data-Centric, Embrace DataOps, Government CIO Outlook

For a few years now, local, state, and federal governments have been embracing agile software development and integration of DevOps into their work. These concepts have grown in popularity as various levels of government have sought to replace legacy systems or develop new systems.

DataOps — new kid on the data block, Vesa Hammarberg, January 11, 2019

When meeting customers that are starting to build new data warehouses and platforms, I have started hearing the requirement that “We want our solution to follow DataOps principles” or “to be DataOps compatible”.

Machine Learning Needs DataOps, Lothar Schubert, January 8, 2019

Data science’s dark secret is that only a small percentage of Big Data projects ever see the light of day in day-to-day business operations. That is despite the hype, and often significant efforts by IT and business teams.

DataOps is the Solution to Data Challenges, John Schmidt, January 13, 2019

The key capability from DataOps is that dataflows morph from an engineering activity into an operational activity which solves a host of challenges from traditional data management.

DataOps — The Way to Eat an Elephant!, David Hill, January 14, 2019

Over the last year or so the term DataOps seems to be becoming more prevalent in the industry as a way of tackling data projects, I have to admit I am a big fan of the concept.For those of you who haven’t been initiated, this is the concept of DevOps for data projects.

Why companies are failing at DataOps — and what to do about it, William Merchan, January 30, 2019

There are a lot of considerations to made when implementing a DataOps model. But with the amount of data across the globe expected to hit 44 zettabytes by 2020, managing and leveraging it efficiently will invariably make or break companies. Don’t let the potential complications of embracing DataOps be dissuading. Agile, collaborative data management is a practice worth cultivating.

Executive summary: DataOps and DevOps for Big Data, Ram Prakash, January 29, 2019

DataOps provides a controlled, integrated, and quality process to capture, store, manage, compute, analyze, visualize, and consume the data. ETL tools help data replication to hydrate data lakes, sandboxes, and data marts.

DataOps and the DataOps Manifesto, Corinium on Jan 30, 2019

DataOps is built upon a foundation that includes Agile Software Development, DevOps and statistical process controls (SPC). Agile development and DevOps have enabled IT and software development organizations to advance from performing one release about every twelve months, in the 1980s, to releasing code many times per hour today. SPC guards against failures, controls quality and provides real-time alerts when data metrics drift outside defined limits. DataOps is a rapid-response, flexible and robust data-analytics capability, which is able to keep up with the creativity of internal stakeholders and users.

The Difference Between DevOps and DataOps, Samuel Ward-Riggs

First DevOps, now DataOps (and ArchOps, DevSecOps, and WinOps!?). You would be forgiven for thinking that the tech hipsters have started a campaign of random capitalization, emulating their favourite i-products and the eXPeriments carried out at the turn of the ME-llennium. But make no mistake: DevOps is a step-change in the delivery capability of software teams, and now DataOps promises the same benefits in the world of Data and Analytics. But before we examine the difference between DevOps and DataOps, let me set the scene…

A Glimpse into the Future of Modern Data Architectures with DataOps at its Core, Itamar Ankorion, February 4, 2019

This year we will see DataOps begin to gain popularity amongst data teams. It will be a time in which IT educates itself on how to put together this collaborative Data Management process and approach, as enterprises seek to make data seamlessly and continuously available, with faster initial delivery and rapid improvement cycles.

Data Ops and the Polar Vortex, Jim Barker, January 29, 2019

There is a growing consensus on what DataOps is, as originally stated in the DataOps manifesto but morphing into something completely different. The DataOps manifesto can be found here and is worth a quick read: https://dataopsmanifesto.org/ but it states that DataOps is focused on analytics and in my view that is only one piece of the puzzle. There is an operations element that is equally important and getting DataOps fully functional addresses both Operational and Analytical needs, that is an important part to keep in mind. You must keep data right for operational benefits to keep a company afloat, and analytics will get benefit out of the operational focus.

How to Build a DataOps Team: 3 Key Team Functions, Mark Marinelli, February 11, 2019

Imagine what you could accomplish if users in your organization had high-quality data at their fingertips that they didn’t need to prepare themselves. What if your organization could answer questions such as “Who are our suppliers?” consistently and completely? Even answering simple questions such as this can result in a competitive edge.

We Wrote a Book on DataOps, DataKitchen, February 11, 2019

In the early 2000s, Chris and Gil worked at a company that specialized in analytics for the pharmaceutical industry. It was a small company that offered a full suite of services related to analytics — data engineering, data integration, visualization and what is now called “data science.” Their customers were marketing and sales executives who tend to be challenging because they are busy, need fast answers and don’t understand or care about the underlying mechanics of analytics. They are business people, not technologists.

Understanding DataOps & DevOps: Different approach but same goal, Nenshad Bardoliwalla, March 11, 2019

If DevOps can improve collaboration and accelerate service and product delivery, could a similar methodology be applied to the help streamline the chaos that is associated with ingesting, transforming, analyzing, and delivering insights from enterprise data? The answer is a resounding yes, and is embodied by the burgeoning new movement known as DataOps.

Qu’est ce que le DataOps ?, Nicolas Risi, 14 Février 2019

Aujourd’hui, les initiatives autour de la donnée se multiplient. Les champs du big data et de la datascience ont amené de nombreux cas d’usages, comme la maintenance prédictive, classification de mail, segmentation client, grâce à la quantité de données disponibles et des derniers modèles statistiques. Mais le chemin jusqu’à la mise en production est souvent délicat et difficile, si bien que de nombreux projets s’arrêtent au stade du POC (Proof Of Concept). C’est là qu’intervient le DataOps.

The DataOps Enterprise Software Industry, 2019, DataKitchen, February 28, 2019

Growing enterprise interest in DataOps has spawned a robust ecosystem of vendors. To date, over $50M has been invested in companies who market a wide array of DataOps product and services.

DataOps and a Three Legged Stool, Dennis Layton, March 17, 2019

A new technology is always the catalyst for change, then come the technical skills but until the appropriate practices and methods for designing, developing, testing solutions are in place, business value is a long way off. Some new technology barely causes a ripple because it is an advancement of what was. Other new technology requires a fundamental shift in the way in which we apply it.

From Wobbly Data Science Project to Efficient DataOps, Andrew Wong, March 18, 2019

I have been grounded on a discipline way of running software development lifecycle (SDLC in short). This is essentially focusing on technical excellence, and collaboration between Product Owner, Developers, Testers, DevOps, Solution Architects are highly embedded in daily software engineering practice. This start my search for similar practices or use cases within Data Science space.

7 Steps to Go From Data Science to Data Ops, Elizabeth Wallace, March 19, 2019

Not too long ago, data operation wasn’t on the radar, but now that it’s all people talk about, how can you move efficiently from data science to data ops? Gil Benghiat, co-founder of Data Kitchen, shares seven steps to do just that.

5 Mistakes You’re Making With DataOps, Elizabeth Wallace, March 29, 2019

DataOps stands to do to data what DevOps did to development. Changing to DataOps isn’t just DevOps though. Whole manifestos center around getting businesses ready to switch the DataOps, but some pitfalls still exist and many businesses fail. Let’s take a look at five common mistakes you could be making with your DataOps and how that could be affecting your success.