ETL (Extract Transform and Load) was the SOP for data integration for 25+ years. A decade ago the introduction of data lakes pushed transformation to the end of the process and into tools like Snowflake, BigQuery, and Redshift.  Now the latest chatter in the data management industry is Reverse ETL. Shouldn’t we call this LTE?

Join Tim, Juan and special guest Tejas Manohar, CEO of Hightouch for a conversation about Reverse ETL and why it matters now.

This episode will feature:
The evolution of data integration pipelines
Use cases for Reverse ETL
What other acronyms make you smh?

ETL (Extract Transform and Load) was the SOP for data integration for 25+ years. A decade ago the introduction of data lakes pushed transformation to the end of the process and into tools like Snowflake, BigQuery, and Redshift. Now the latest chatter in the data management industry is Reverse ETL. Shouldn’t we call this LTE?
Join Tim, Juan and special guest Tejas Manohar, CEO of Hightouch for a conversation about Reverse ETL and why it matters now.
This episode will feature:
<ul><li>The evolution of data integration pipelines</li>
 <li>Use cases for Reverse ETL</li>
 <li>What other acronyms make you smh?</li>
</ul>

Catalog and Cocktails is the honest, No-BS, non-sales-y podcast about data and analytics. This is your unfiltered chat about everything interesting in data and analytics. Join Juan Sequeda and Tim Gasper to explore emerging topics and hear from thought leaders, practitioners and executives across the data and analytics space.

Catalog and Cocktails is the honest, No-BS, non-sales-y podcast about data and analytics. This is your unfiltered chat about everything interesting in data and analytics. Join Juan Sequeda and Tim Gasper to explore emerging topics and hear from thought leaders, practitioners and executives across the data and analytics space.

Catalog & Cocktails: The Honest, No-BS Data Podcast

What’s the deal with Reverse ETL? w/ Tejas Manohar

Most quantum conversations are stuck on the timeline question: five years out? Two? Q-Day, the day current RSA encryption breaks, keeps getting pulled forward. But the actual risk to your data is already here. Adversaries are harvesting encrypted data today to decrypt it once quantum gets there. That makes this a data management problem right now.

John Licata, ServiceNow's futures director and quantum lead joins Juan and Tim, to unpack what this actually means for enterprise data teams. They get into where quantum computing does things classical AI genuinely can't do well: drug discovery, supply chain simulation, personalized medicine, where the quantum + AI combination changes everything, and who even owns quantum strategy? CISO? COOs? But the real argument is on the business value topic, not just focusing on risk mitigation which is where the real argument lives right now.

Most quantum conversations are stuck on the timeline question: five years out? Two? Q-Day, the day current RSA encryption breaks, keeps getting pulled forward. But the actual risk to your data is already here. Adversaries are harvesting encrypted data today to decrypt it once quantum gets there. That makes this a data management problem right now.
John Licata, ServiceNow's futures director and quantum lead joins Juan and Tim, to unpack what this actually means for enterprise data teams. They get into where quantum computing does things classical AI genuinely can't do well: drug discovery, supply chain simulation, personalized medicine, where the quantum + AI combination changes everything, and who even owns quantum strategy? CISO? COOs? But the real argument is on the business value topic, not just focusing on risk mitigation which is where the real argument lives right now.

Harvest Now, Decrypt Later: Why Quantum Is Already a Data Problem with John Licata

This is the takeaway episode with John Licata, ServiceNow's futures director and quantum lead where Juan and Tim unpack what quantum means for enterprise data teams.They get into where quantum computing does things classical AI genuinely can't do well: drug discovery, supply chain simulation, personalized medicine, where the quantum + AI combination changes everything, and who even owns quantum strategy? CISO? COOs? But the real argument is on the business value topic, not just focusing on risk mitigation which is where the real argument lives right now.

This is the takeaway episode with John Licata, ServiceNow's futures director and quantum lead where Juan and Tim unpack what quantum means for enterprise data teams.They get into where quantum computing does things classical AI genuinely can't do well: drug discovery, supply chain simulation, personalized medicine, where the quantum + AI combination changes everything, and who even owns quantum strategy? CISO? COOs? But the real argument is on the business value topic, not just focusing on risk mitigation which is where the real argument lives right now.

TAKEAWAY - Harvest Now, Decrypt Later: Why Quantum Is Already a Data Problem with John Licata

Shachar MEIR has spent 20 years fixing data teams and understanding why they fail. In this live episode, Shachar joins Juan and Tim to discuss the reason why they fail and what they should be doing in order to succeed. 

Shachar argues that data leaders default to what they can control (cough cough technology_ and underinvest in people, process, and culture. A new CDO comes in, migrates legacy databases to a cloud data platform, and two years later you have the same problems on a better platform.

We get into what's actually missing: building trust in data (which takes time and breaks fast), creating incentives so people actually use what data teams build, and spending real time in the business which is not asking what dashboards they need, but what problems they have. 

Topics discussed: 
Technology is not why data teams fail
Why data leaders default to technology
People, process and CULTURE
Data trust
Incentives
Understanding the business
Dashboard bloat
Self-service analytics
Dashboards aren't dead
What CDOs do well and what they miss

Shachar MEIR has spent 20 years fixing data teams and understanding why they fail. In this live episode, Shachar joins Juan and Tim to discuss the reason why they fail and what they should be doing in order to succeed.&nbsp;
Shachar argues that data leaders default to what they can control (cough cough technology_ and underinvest in people, process, and culture. A new CDO comes in, migrates legacy databases to a cloud data platform, and two years later you have the same problems on a better platform.
We get into what's actually missing: building trust in data (which takes time and breaks fast), creating incentives so people actually use what data teams build, and spending real time in the business which is not asking what dashboards they need, but what problems they have.&nbsp;
Topics discussed:&nbsp;
<ul>
<li aria-level="1">Technology is not why data teams fail</li>
<li aria-level="1">Why data leaders default to technology</li>
<li aria-level="1">People, process and CULTURE</li>
<li aria-level="1">Data trust</li>
<li aria-level="1">Incentives</li>
<li aria-level="1">Understanding the business</li>
<li aria-level="1">Dashboard bloat</li>
<li aria-level="1">Self-service analytics</li>
<li aria-level="1">Dashboards aren't dead</li>
<li aria-level="1">What CDOs do well and what they miss</li>
</ul>

What’s the deal with Reverse ETL? w/ Tejas Manohar

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