Name: Modernizing Your Data with DataOps
Start: 2021-04-20T13:00:00Z
End: 2021-04-20T13:00:58.000Z
Location: BrightTALK

Data Architecture, Data Engineering, Data Analytics, DataOps - Mphasis Datalytyx provides professional data services and consulting to modernize data architecture and processes for speed, agility and trust.

Contact us at info@datalytyx.com to speak with a member of our team and learn how we combine data technology, expertise and best practices to get value from your data faster.

Developing Data Products like Software Products with Talend and DataOps

Snowflake on Steroids

In this video, we'll walk you through the 'Getting Started with Snowflake - Zero to Snowflake' entry-level guide in only 58 minutes.  Salome will demonstrate how easy it is to prepare your lab environment, load data, run queries, clone data, work with semi-structured data, time travel, set up role-based access controls, get data from the Data Marketplace, and reset your environment  They say it will take 90 minutes, but she'll get this done in 58.  You can follow along using your own Snowflake trial by using Snowflake's full guide available here: https://quickstarts.snowflake.com/guide/getting_started_with_snowflake/index.html#0  If you want to get started with Snowflake and need some guidance, schedule time with us here: https://calendly.com/datalytyx/snowflake-call

Zero to Snowflake in 58 minutes

DataOps is just DevOps, right? Hold on. DevOps methodology is a good starting point to understand DataOps, but there's a lot of Data specific considerations that DevOps just can't address. From obtaining realistic test data to complying with data regulations, DevOps just doesn't cover what data engineers need to think about.

DataOps vs DevOps

Tom, Matt and Will pick up where they left off on the 2017 article by Maxime Beauchemin, The Downfall of the Data Engineer: https://maximebeauchemin.medium.com/the-downfall-of-the-data-engineer-5bfb701e5d6b

They discuss...
-ETL tools vs coding
-the hype around DataOps
-getting test data
-the place at the table that data engineers have
-where data science investment should go
-the barrier to entry for data engineers
-operational creep
-software engineers vs. data engineers
-and how people become data engineers

Data Engibeering is a weekly conversation with data professionals about data, architecture, best practices and technology. Visit us at www.datalytyx.com or email info@datalytyx.com to get in touch.

The opinions expressed in these conversations are those of the individuals and don't necessarily reflect the opinions of Mphasis Datalytyx or its parent company, Mphasis.

Data Engibeering: The Downfall of the Data Engineer - Part Deux

Tom, Matt and Will discuss the 2017 article by Maxime Beauchemin, The Downfall of the Data Engineer: https://maximebeauchemin.medium.com/the-downfall-of-the-data-engineer-5bfb701e5d6b

They discuss...
-the problems data engineers still face
-how things have changed since 2017
-new best practices
-pipeline constipation
-and change management.

Data Engibeering is a weekly conversation with data professionals about data, architecture, best practices and technology. Visit us at www.datalytyx.com or email info@datalytyx.com to get in touch.

The opinions expressed in these conversations are those of the individuals and don't necessarily reflect the opinions of Mphasis Datalytyx or its parent company, Mphasis.

Data Engibeering: The Downfall of the Data Engineer - Part I

Tom and Matt discuss the concept of Data Mesh as explained by Zhamak Dehghani in this video: https://youtu.be/L_-fHo0ZkAo

Tom also pulls up a diagram from this article on Dataception.com: http://dataception.com/blogpost.html?blogName=Data-Mesh-Deploying-Data-Products-at-the-speed-of-the-business

They discuss
- the concept of a Data Mesh
- what it means for Data Engineers
- some of the issues that might come up if it's implemented
- what they like about it
- and predictions for the future.

Data Engibeering is a weekly conversation with data professionals about data, architecture, best practices and technology. Visit us at www.datalytyx.com or email info@datalytyx.com to get in touch.

The opinions expressed in these conversations are those of the individuals and don't necessarily reflect the opinions of Mphasis Datalytyx or its parent company, Mphasis.

Data Engibeering: Data Mesh - A Personal Attack

Tom, Dan and Matt discuss the pronouncements that Data Lakes are no longer part of the modern data stack.

They discuss
- what a Data Lake really is
- should we even use the term
- the pros and cons of a Data Lake
- and the differences between a Data Lake and doing ELT on a modern data warehouse

We reference:
This interview with Jon Cooke from DataCeption 
https://www.linkedin.com/posts/activity-6780550563261165568-6G4j

This article by Thomas Spicer from OpenBridge
https://towardsdatascience.com/sorry-data-lakes-are-not-legacy-625bc70b4090

Data Engibeering is a weekly conversation with data professionals about data, architecture, best practices and technology. Visit us at www.datalytyx.com or email info@datalytyx.com to get in touch.

The opinions expressed in these conversations are those of the individuals and don't necessarily reflect the opinions of Mphasis Datalytyx or its parent company, Mphasis.

Data Engibeering: Are Data Lakes Really Dead?

Forrester VP, Michele Goetz leads this expert panel discussion on Modernizing Data with Dataops which includes Mike Penrose, Practice Lead, Mphasis Datalytyx, Frank Thomas, Head of EMEA Channel, Talend, Tony Young, Senior Manager of Technology and Cloud, Snowflake and Baz Javanshiri, Director of Financial Planning & Analysis, Stonegate Pub Company.

For more information from us or any of the panelists, please contact us at info@datalytyx.com

Modernizing Your Data with DataOps

DataOps

Data Architecture

Talend

Data Engineering

pubs

Snowflake

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Modernizing Your Data with DataOps

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Mphasis Datalytyx