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    Technology

    Data Engineering Weekly

    The Weekly Data Engineering Newsletter

    www.dataengineeringweekly.com

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    Copyright: © Data Engineering Weekly

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    Latest Episodes:
    DEW #120: The Case for Data Contracts, Action-Position data quality assessment framework & Stop emphasizing the Data Catalog Mar 12, 2023
    Show notes

    Please read Data Engineering Weekly Edition #120

    Topic 1: Colin Campbell: The Case for Data Contracts - Preventative data quality rather than reactive data quality

    In this episode, we focus on the importance of data contracts in preventing data quality issues. We discuss an article by Colin Campbell highlighting the need for a data catalog and the market scope for data contract solutions. We also touch on the idea that data creation will be a decentralized process and the role of tools like data contracts in enabling successful decentralized data modeling. We emphasize the importance of creating high-quality data and the need for technological and organizational solutions to achieve this goal.

    Key highlights of the conversation

    • "Preventative data quality rather than reactive data quality. It should start with contracts." - Colin Campbell. - Author of the article
    • "Contracts put a preventive structure in place" - Ashwin.
    • "The successful data-driven companies all do one thing very well. They create high-quality data." - Ananth.

    Link:

    https://uncomfortablyidiosyncratic.substack.com/p/the-case-for-data-contracts

    https://www.dataengineeringweekly.com/p/introducing-schemata-a-decentralized


    Topic 2: Yerachmiel Feltzman: Action-Position data quality assessment framework

    In this conversation, we discuss a framework for data quality assessment called the Action Position framework. The framework helps define what actions should be taken based on the severity of the data quality problem. We also discuss two patterns for data quality: Write-Audit-Publish (WAP) and Audit-Write-Publish (AWP). The WAP pattern involves writing data, auditing it, and publishing it, while the AWP pattern involves auditing data, writing it, and publishing it. We encourage readers to share their best practices for addressing data quality issues.

    Are you using any Data Quality framework in your organization? Do you have any best practices on how you address data quality issues? What do you think of the action-position data quality framework? Please add your comments in the SubStack chat.

    Link:

    https://medium.com/everything-full-stack/action-position-data-quality-assessment-framework-d833f6b77b7

    Dremio WAP pattern: https://www.dremio.com/resources/webinars/the-write-audit-publish-pattern-via-apache-iceberg/


    Topic 3: Guy Fighel - Stop emphasizing the Data Catalog

    We discuss the limitations of data catalogs and the author’s view on the semantic layer as an alternative. The author argues that data catalogs are passive and quickly become outdated and that a stronger contract with enforced data quality could be a better solution. We also highlight the cost factors of implementing a data catalog and suggest that a more decentralized approach may be necessary to keep up with the increasing number of data sources. Innovation in this space is needed to improve organizations' discoverability and consumption of data assets.

    Link:

    https://www.linkedin.com/pulse/stop-emphasizing-data-catalog-guy-fighel/

    https://www.dataengineeringweekly.com/p/data-catalog-a-broken-promise



    Data Engineering Weekly Radio #120 Mar 12, 2023
    Show notes

    We are back in our Data Engineering Weekly Radio for edition #120. We will take 2 or 3 articles from each week's Data Engineering Weekly edition and go through an in-depth analysis.

    From editor #120, we took the following articles

    Topic 1: Colin Campbell: The Case for Data Contracts - Preventative data quality rather than reactive data quality

    In this episode, we focus on the importance of data contracts in preventing data quality issues. We discuss an article by Colin Campbell highlighting the need for a data catalog and the market scope for data contract solutions. We also touch on the idea that data creation will be a decentralized process and the role of tools like data contracts in enabling successful decentralized data modeling. We emphasize the importance of creating high-quality data and the need for technological and organizational solutions to achieve this goal.

    Key highlights of the conversation

    "Preventative data quality rather than reactive data quality. It should start with contracts." - Colin Campbell. - Author of the article

    "Contracts put a preventive structure in place" - Ashwin.

    "The successful data-driven companies all do one thing very well. They create high-quality data." - Ananth.

    Ananth’s post on Schemata

    Topic 2: Yerachmiel Feltzman: Action-Position data quality assessment framework

    In this conversation, we discuss a framework for data quality assessment called the Action Position framework. The framework helps define what actions should be taken based on the severity of the data quality problem. We also discuss two patterns for data quality: Write-Audit-Publish (WAP) and Audit-Write-Publish (AWP). The WAP pattern involves writing data, auditing it, and publishing it, while the AWP pattern involves auditing data, writing it, and publishing it. We encourage readers to share their best practices for addressing data quality issues.

    Are you using any Data Quality framework in your organization? Do you have any best practices on how you address data quality issues? What do you think of the action-position data quality framework? Please add your comments in the SubStack chat.

    https://medium.com/everything-full-stack/action-position-data-quality-assessment-framework-d833f6b77b7

    Dremio WAP pattern: https://www.dremio.com/resources/webinars/the-write-audit-publish-pattern-via-apache-iceberg/

    Topic 3: Guy Fighel - Stop emphasizing the Data Catalog

    We discuss the limitations of data catalogs and the author’s view on the semantic layer as an alternative. The author argues that data catalogs are passive and quickly become outdated and that a stronger contract with enforced data quality could be a better solution. We also highlight the cost factors of implementing a data catalog and suggest that a more decentralized approach may be necessary to keep up with the increasing number of data sources. Innovation in this space is needed to improve organizations' discoverability and consumption of data assets.

    Something to think about in this conversation

    "If you don't catalog everything and we only catalog what is required for the purpose of business decision-making, does that solve the data catalog problem in an organization?"

    https://www.linkedin.com/pulse/stop-emphasizing-data-catalog-guy-fighel/


    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dataengineeringweekly.com

    Podcast: Data Engineering Weekly #119 Mar 08, 2023
    Show notes

    We are super excited to be back to discussing Data Engineering Weekly Newsletter articles every week. We will take 2 or 3 articles from each week's Data Engineering Weekly edition and go through an in-depth analysis.

    On Data Engineering Weekly edition #119, We are taking three articles.

    #1 Netflix's article about Scaling Media Machine Learning at Netflix

    https://netflixtechblog.com/scaling-media-machine-learning-at-netflix-f19b400243

    #2 Alex Woodie's article about Open Table Formats Square Off in Lakehouse Data Smackdown

    https://www.datanami.com/2023/02/15/open-table-formats-square-off-in-lakehouse-data-smackdown/

    #3 Plum Living's article about Building a semantic layer in Preset (Superset) with dbt

    https://medium.com/plum-living/building-a-semantic-layer-in-preset-superset-with-dbt-71ee3238fc20

    We referenced David Jayatillake's article about Metricalypse in the show.


    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.dataengineeringweekly.com

    DEW #119: Netflix's Scaling Media Machine Learning at Netflix, Open Table Formats Square Off in Lakehouse Data Smackdown & Building a semantic layer in Preset (Superset) with dbt Mar 06, 2023
    Show notes

    We are super excited to be back to discussing Data Engineering Weekly Newsletter articles every week. We will take 2 or 3 articles from each week's Data Engineering Weekly edition and go through an in-depth analysis.


    On Data Engineering Weekly edition #119, We are taking three articles.


    • #1 Netflix's article about Scaling Media Machine Learning at Netflix

    https://netflixtechblog.com/scaling-media-machine-learning-at-netflix-f19b400243


    • #2 Alex Woodie's article about Open Table Formats Square Off in Lakehouse Data Smackdown

    https://www.datanami.com/2023/02/15/open-table-formats-square-off-in-lakehouse-data-smackdown/


    • #3 Plum Living's article about Building a semantic layer in Preset (Superset) with dbt

    https://medium.com/plum-living/building-a-semantic-layer-in-preset-superset-with-dbt-71ee3238fc20


    We referenced David Jayatillake's article about Metricalypse in the show.

    https://davidsj.substack.com/p/metricalypse-now


    Data Engineering Weekly #75 Feb 21, 2022
    Show notes

    I am sharing my thoughts around the 75th edition of the Data Engineering Weekly newsletter. You can read the edition here https://www.dataengineeringweekly.com/p/data-engineering-weekly-75 The featured articles this week are, 📚 Dagster: Bundling Vs UnBundling the Data Platform 📚 Prefect: Logs, the Prefect Way 📚 Pinterest: Spinner - Pinterest’s Workflow Platform 📚 Apache Arrow: Introducing Apache Arrow Flight SQL - Accelerating Database Access 📚 Kevin Kho: Introducing Fugue — Reducing PySpark Developer Friction 📚 Mikkel Dengsøe: Data, engineers, and designers - How the US compares to Europe 📚 Zach Quinn: Why Data Engineers Must Have Domain Knowledge — And How To Gain It 📚 Salesforce: Embracing Mutable Big Data 📚 Microsoft DataScience: Natural Language Understanding What’s the purpose of meaning? 📚 Back Market: From Delta Lake to BigQuery 📚 Foodpanda: How foodpanda reduced 45% of our BigQuery cost with reservations slots 📚 Hifly Labs: Awesome dbt


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