Datafold Blog
Exploring how to do data better
Exploring how to do data better
Introducing No-Code CI in Datafold: Easily integrate data diffing into your CI workflowâregardless of your data transformation and orchestration toolingâfor improved data quality, proactive issue detection, and streamlined PR reviews.
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Introducing No-Code CI in Datafold: Easily integrate data diffing into your CI workflowâregardless of your data transformation and orchestration toolingâfor improved data quality, proactive issue detection, and streamlined PR reviews.
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Learn how Datafold automates proactive data quality testing in CI to ensure data integrity.
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Learn how Datafold automates proactive data quality testing in CI to ensure data integrity.
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Explore how a data quality scorecard ensures high-quality, trustworthy data management.
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Explore how a data quality scorecard ensures high-quality, trustworthy data management.
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Learn how dbt enhances ETL processes and enables more efficient data workflows.
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Learn how dbt enhances ETL processes and enables more efficient data workflows.
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Understand why a data quality scorecard looks different for every team, how to craft one aligned to your data management goals.
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Understand why a data quality scorecard looks different for every team, how to craft one aligned to your data management goals.
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Learn best practices for debugging, common CI failures, and strategies for improving your CI data pipeline.
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Learn best practices for debugging, common CI failures, and strategies for improving your CI data pipeline.
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Learn how dbt seeds enable you to leverage static data in your dbt project.
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Learn how dbt seeds enable you to leverage static data in your dbt project.
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Data teams can now use pre-committed Google Cloud spend to acquire Datafold directly through the Google Cloud Marketplace.
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Data teams can now use pre-committed Google Cloud spend to acquire Datafold directly through the Google Cloud Marketplace.
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Discover the role of metadata in data catalogs, how they organize and enhance data accessibility, and why they're essential for effective data management.
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Discover the role of metadata in data catalogs, how they organize and enhance data accessibility, and why they're essential for effective data management.
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Understand how Datafold's value-level data diffs detect and prevent unintended changes to immutable data.
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Understand how Datafold's value-level data diffs detect and prevent unintended changes to immutable data.
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Explore the crucial role of data validation in maintaining accuracy and trustworthiness within your data ecosystem, from source to target.
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Explore the crucial role of data validation in maintaining accuracy and trustworthiness within your data ecosystem, from source to target.
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Discover the critical distinctions between data integrity and data quality and learn practical strategies for ensuring accuracy and reliability in your data ecosystem.
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Discover the critical distinctions between data integrity and data quality and learn practical strategies for ensuring accuracy and reliability in your data ecosystem.
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Announcing the sunsetting of the open source data-diff package.
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Announcing the sunsetting of the open source data-diff package.
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dbt provides native data lineage capabilities. Learn how and why data teams leverage dbt lineage for data discovery and impact analysis.
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dbt provides native data lineage capabilities. Learn how and why data teams leverage dbt lineage for data discovery and impact analysis.
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Learn more about how dbt Exposures extend your dbt DAG to identify downstream assets like BI dashboards and must be manually defined and maintained in YAML.
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Learn more about how dbt Exposures extend your dbt DAG to identify downstream assets like BI dashboards and must be manually defined and maintained in YAML.
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Learn best practices for how to write and manage dbt tests in your organization.
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Learn best practices for how to write and manage dbt tests in your organization.
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Learn how (and why) to implement dbt unit tests for your dbt project to test niche and complex business logic.
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Learn how (and why) to implement dbt unit tests for your dbt project to test niche and complex business logic.
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Understand the 3 critical differences between Datafold tests and dbt tests, and learn why combining them is crucial for complete data quality testing coverage.
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Understand the 3 critical differences between Datafold tests and dbt tests, and learn why combining them is crucial for complete data quality testing coverage.
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Explore the new Datafold-Dremio integration for faster, reliable data migrations and enhanced dbt testing. Perfect for modernizing data workflows.
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Explore the new Datafold-Dremio integration for faster, reliable data migrations and enhanced dbt testing. Perfect for modernizing data workflows.
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Explore how the dbt Semantic Layer is evolving the way data teams define and expose key business metrics.
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Explore how the dbt Semantic Layer is evolving the way data teams define and expose key business metrics.
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Introducing Monitors in Datafold: scheduled cross-database diffs for continuous source-to-target validation.
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Introducing Monitors in Datafold: scheduled cross-database diffs for continuous source-to-target validation.
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Gleb Mezhanskiy shares why data reconciliation remains so challenging, the surprising link between reconciliation and accounting, and the future of data tooling.
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Gleb Mezhanskiy shares why data reconciliation remains so challenging, the surprising link between reconciliation and accounting, and the future of data tooling.
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Add 4 advanced integrations to your dbt project's CI pipeline to make it more efficient, error-resistant, and collaborative.
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Add 4 advanced integrations to your dbt project's CI pipeline to make it more efficient, error-resistant, and collaborative.
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Now supporting: 10x faster cross-database diffing, real-time diff results, and representative sampling.
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Now supporting: 10x faster cross-database diffing, real-time diff results, and representative sampling.
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Explore data diffs for cloud migration success. Learn how Datafold optimizes your process, ensuring data integrity with precision, speed, and confidence.
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Explore data diffs for cloud migration success. Learn how Datafold optimizes your process, ensuring data integrity with precision, speed, and confidence.
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Overview on getting started with dbt Core via CLI on an M1 Mac.
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Overview on getting started with dbt Core via CLI on an M1 Mac.
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Learn about Datafold's new MySQL integration, so you can perform diffs across databases during a migration or ongoing replication.
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Learn about Datafold's new MySQL integration, so you can perform diffs across databases during a migration or ongoing replication.
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How to validate parity of tables across databases using open source data-diff and Datafold Cloud
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How to validate parity of tables across databases using open source data-diff and Datafold Cloud
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Datafold now supports data diffing for Oracle, so you can migrate and replicate your data with greater speed and confidence.
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Datafold now supports data diffing for Oracle, so you can migrate and replicate your data with greater speed and confidence.
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Create your first CI pipeline through GitHub Actions that you can get up and running for your dbt project in just an afternoon.
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Create your first CI pipeline through GitHub Actions that you can get up and running for your dbt project in just an afternoon.
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Learn how Datafold's cross-database diffing and SQL Translator are accelerating migrations off of SQL Server.
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Learn how Datafold's cross-database diffing and SQL Translator are accelerating migrations off of SQL Server.
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Datafold is now available on Azure Marketplace. Leverage Azure Marketplace benefits to unlock the leading automated data testing tool for dbt, migrations, and data reconciliation.
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Datafold is now available on Azure Marketplace. Leverage Azure Marketplace benefits to unlock the leading automated data testing tool for dbt, migrations, and data reconciliation.
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Dive into our guide on cloud data migration tools. Discover how to choose the right tools for efficient, secure data transfers, and successful migrations.
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Dive into our guide on cloud data migration tools. Discover how to choose the right tools for efficient, secure data transfers, and successful migrations.
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Explore essential strategies for cloud data migrations. Learn about lift and shift, migrate and refactor methods, and effective testing techniques for a seamless transition.
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Explore essential strategies for cloud data migrations. Learn about lift and shift, migrate and refactor methods, and effective testing techniques for a seamless transition.
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How Datafold is now bringing automated data tests to your dbt PRs in Bitbucket and Azure DevOps.
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How Datafold is now bringing automated data tests to your dbt PRs in Bitbucket and Azure DevOps.
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Learn what steps your team needs to take to improve data quality and get the most out of your data.
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Learn what steps your team needs to take to improve data quality and get the most out of your data.
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Data diffing is the process of comparing two datasets. See various ways to compare data at different levels of complexity.
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Data diffing is the process of comparing two datasets. See various ways to compare data at different levels of complexity.
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Uncover the potential of Apache Airflow's most underused features: User-defined Macros, XCom, and Task Sensors, to elevate your data pipeline efficiency.
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Uncover the potential of Apache Airflow's most underused features: User-defined Macros, XCom, and Task Sensors, to elevate your data pipeline efficiency.
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How data teams don't have to sacrifice data quality or deployment velocity
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How data teams don't have to sacrifice data quality or deployment velocity
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Learn the difference between development vs production environments and what you should consider for your own development environment in dbt.
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Learn the difference between development vs production environments and what you should consider for your own development environment in dbt.
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Discover the true meaning of data quality and management with specific dimensions and strategies to achieve measurable success in data engineering.
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Discover the true meaning of data quality and management with specific dimensions and strategies to achieve measurable success in data engineering.
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Explore how to achieve data excellence with our guide on validating and testing ETL/ELT pipelines. Ensure accuracy and trust in your data management.
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Explore how to achieve data excellence with our guide on validating and testing ETL/ELT pipelines. Ensure accuracy and trust in your data management.
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Plan, translate, and validate data across systems seamlessly to massively accelerate your data migration project with new Datafold Cloud features.
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Plan, translate, and validate data across systems seamlessly to massively accelerate your data migration project with new Datafold Cloud features.
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Discover how data quality tools preempt production issues and why observability is key to reactive problem-solving. Equip your team with the right tools.
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Discover how data quality tools preempt production issues and why observability is key to reactive problem-solving. Equip your team with the right tools.
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The technical deep-dive into how you can run Datafold Cloud in CI for your transformation tool or orchestrator of choice.
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The technical deep-dive into how you can run Datafold Cloud in CI for your transformation tool or orchestrator of choice.
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Announcing the Tableau Integration for Datafold Cloudâbringing better governance, visibility, and proactive testing to your dbt project and BI tools.
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Announcing the Tableau Integration for Datafold Cloudâbringing better governance, visibility, and proactive testing to your dbt project and BI tools.
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A review SQL tools for performing data analysis from the command line covering ease of installation, supported data and file formats, SQL functionality, and benchmarking on a common query.
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A review SQL tools for performing data analysis from the command line covering ease of installation, supported data and file formats, SQL functionality, and benchmarking on a common query.
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Learn about eight dimensions of data qualityâaccuracy, completeness, consistency, reliability, timeliness, uniqueness, usefulness, and differenceâfor better decision-making
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Learn about eight dimensions of data qualityâaccuracy, completeness, consistency, reliability, timeliness, uniqueness, usefulness, and differenceâfor better decision-making
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So you want to learn more about dbt Cloud? Go through all the tips, tricks, and best practices to ensure your team is leveraging all dbt Cloud has to offer.
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So you want to learn more about dbt Cloud? Go through all the tips, tricks, and best practices to ensure your team is leveraging all dbt Cloud has to offer.
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Dive into the significance of automated regression testing in data analytics engineering. Discover how top-tier data teams harness these software engineering practices to guarantee data quality and efficiency.
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Dive into the significance of automated regression testing in data analytics engineering. Discover how top-tier data teams harness these software engineering practices to guarantee data quality and efficiency.
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Bad data cost Samsung and Uber ridiculous sums of money with issues that could have been averted if they had been invested in data quality management. Read about their mistakes, and see how you could avoid doing the same.
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Bad data cost Samsung and Uber ridiculous sums of money with issues that could have been averted if they had been invested in data quality management. Read about their mistakes, and see how you could avoid doing the same.
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Discover the significance of choosing the right data lineage tools for analytics. Learn how to enhance your data lineage visualization for more insightful results.
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Discover the significance of choosing the right data lineage tools for analytics. Learn how to enhance your data lineage visualization for more insightful results.
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All the sessions, events, and vibes the Datafold team is looking forward to at Coalesce 2023.
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All the sessions, events, and vibes the Datafold team is looking forward to at Coalesce 2023.
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Explore how data lineage can transform your data quality. Dive into its significance, tools, and best practices to ensure integrity, traceability, and trustworthiness in your data ecosystem
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Explore how data lineage can transform your data quality. Dive into its significance, tools, and best practices to ensure integrity, traceability, and trustworthiness in your data ecosystem
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Without proactive data quality management, mistakes will happen. What you do can help improve your data quality in the future. Data quality post-mortems are a valuable tool for building improved processes and systems, plus rebuilding stakeholder trust.
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Without proactive data quality management, mistakes will happen. What you do can help improve your data quality in the future. Data quality post-mortems are a valuable tool for building improved processes and systems, plus rebuilding stakeholder trust.
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Unpacking why and how data teams undergo data replication, and best practices to ensure high data quality.
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Unpacking why and how data teams undergo data replication, and best practices to ensure high data quality.
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Why do more than 80% of all migrations fail? And how Datafold is helping change that statistic.
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Why do more than 80% of all migrations fail? And how Datafold is helping change that statistic.
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A deep-dive into Datafold Cloud's core features and use cases.
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A deep-dive into Datafold Cloud's core features and use cases.
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We're unpacking the tech stack, pain points, and team set-up that make Datafold a strong fit for your team.
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We're unpacking the tech stack, pain points, and team set-up that make Datafold a strong fit for your team.
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Github offers many different features such as protected branches, pull requests, and code reviews. It is best practice to take advantage of all of these in your analytics workflow and use them to maintain the integrity of your data models.
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Github offers many different features such as protected branches, pull requests, and code reviews. It is best practice to take advantage of all of these in your analytics workflow and use them to maintain the integrity of your data models.
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How Datafold Cloud is preventing bad data from slipping into the tools that matter to you, your team, and your business.
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How Datafold Cloud is preventing bad data from slipping into the tools that matter to you, your team, and your business.
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This post is about the good and the not-as-good of Looker and Tableau, so you feel empowered to choose the tool that will best serve your use cases and users.
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This post is about the good and the not-as-good of Looker and Tableau, so you feel empowered to choose the tool that will best serve your use cases and users.
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If you're looking to build the ideal modern data stack for analytics using only open-source options, this is the blog for you. Find all the best open-source alternatives to your favorite paid tools.
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If you're looking to build the ideal modern data stack for analytics using only open-source options, this is the blog for you. Find all the best open-source alternatives to your favorite paid tools.
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When you've done all your data tests and data quality issues are still happening, what's happening? We unpack why these issues happen and how to know when it's time to evolve the way you test your data.
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When you've done all your data tests and data quality issues are still happening, what's happening? We unpack why these issues happen and how to know when it's time to evolve the way you test your data.
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Data teams on dbt who donât have CI configured are missing out massively.
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Data teams on dbt who donât have CI configured are missing out massively.
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We're unpacking all things data diffingâwhat it is, how it differs from standard dbt tests, and why your team should be using it to test your data.
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We're unpacking all things data diffingâwhat it is, how it differs from standard dbt tests, and why your team should be using it to test your data.
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Put a comma in the right place
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Put a comma in the right place
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Lightdash is an open-source alternative to Looker that natively integrates with dbt. It may not be as mature as other open-source products like Metabase, Querybook, or Superset, but it is different in a few essential ways.
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Lightdash is an open-source alternative to Looker that natively integrates with dbt. It may not be as mature as other open-source products like Metabase, Querybook, or Superset, but it is different in a few essential ways.
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Objective criteria and subjective advice when choosing a data warehouse for analytics.
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Objective criteria and subjective advice when choosing a data warehouse for analytics.
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Why write complicated logic in dbt when you can use someone elseâs? This post will run through how to install and use some popular (and some unsung) dbt utils in your project.
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Why write complicated logic in dbt when you can use someone elseâs? This post will run through how to install and use some popular (and some unsung) dbt utils in your project.
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dbt and Snowflake are not only two of the most popular modern data stack tools, but they are the most powerful when used correctly. They also happen to be two of my favorite tools to use together. Both contain features that allow you to truly customize your data warehouse and data models, ensuring that they operate seamlessly together. âIn this article, weâll take a look at a bunch of different ways to get the most out of your dbt + Snowflake setup.
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dbt and Snowflake are not only two of the most popular modern data stack tools, but they are the most powerful when used correctly. They also happen to be two of my favorite tools to use together. Both contain features that allow you to truly customize your data warehouse and data models, ensuring that they operate seamlessly together. âIn this article, weâll take a look at a bunch of different ways to get the most out of your dbt + Snowflake setup.
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Datafold is now available on the AWS Marketplace. Leverage AWS Marketplace benefits to unlock the leading automated data testing tool.
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Datafold is now available on the AWS Marketplace. Leverage AWS Marketplace benefits to unlock the leading automated data testing tool.
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Get an overview of Data Quality Meetup #9. With speakers from Capital One, FINN Auto, Airbytes, Virgin Media O2 and dbt Labs. The Meetup was focused on "Running dbt at scale", where a variety of approaches were discussed across small startups to enterprise-sized organizations. Each speaker presented during the lightning talks round, which later transitioned into the panel discussion.
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Get an overview of Data Quality Meetup #9. With speakers from Capital One, FINN Auto, Airbytes, Virgin Media O2 and dbt Labs. The Meetup was focused on "Running dbt at scale", where a variety of approaches were discussed across small startups to enterprise-sized organizations. Each speaker presented during the lightning talks round, which later transitioned into the panel discussion.
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Once Datafold made their first dedicated data hire (me), the volume and frequency of contributions to our dbt project went through the roof. Our single Github Actions job was doing a full build of our project with every pull request I opened, and for each merge. This posed problems: specifically, long feedback loops on CI, and an increased Snowflake bill.
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Once Datafold made their first dedicated data hire (me), the volume and frequency of contributions to our dbt project went through the roof. Our single Github Actions job was doing a full build of our project with every pull request I opened, and for each merge. This posed problems: specifically, long feedback loops on CI, and an increased Snowflake bill.
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This blog outlines how to setup a dbt Cloud PR Job in CIâ, which enables building dbt models and running dbt tests whenever you push commits to your GitHub (or GitLab) Pull (or Merge) Request.
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This blog outlines how to setup a dbt Cloud PR Job in CIâ, which enables building dbt models and running dbt tests whenever you push commits to your GitHub (or GitLab) Pull (or Merge) Request.
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Why we created a Development Testing suite for dbt and how to get started with the open source data-diff dbt integration, as well as a brief introduction of Datafold Cloud.
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Why we created a Development Testing suite for dbt and how to get started with the open source data-diff dbt integration, as well as a brief introduction of Datafold Cloud.
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Learn how to add freshness tests to your dbt sources, determine freshness values and use the dbt source freshness command.
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Learn how to add freshness tests to your dbt sources, determine freshness values and use the dbt source freshness command.
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Learn about common challenges and solutions to test data pipelines that spread across multiple layers and tools. In the future, data testing efforts may consolidate on the transformation layer while the orchestration layer simplifies creating testing environments.
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Learn about common challenges and solutions to test data pipelines that spread across multiple layers and tools. In the future, data testing efforts may consolidate on the transformation layer while the orchestration layer simplifies creating testing environments.
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Learn how to validate dbt model changes with dbt's audit_helper package and Datafold's data-diff CLI.
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Learn how to validate dbt model changes with dbt's audit_helper package and Datafold's data-diff CLI.
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Learn how to test and validate dbt code changes in Snowflake with data-diff. After building your dbt models with 'dbt run' in development you can run the 'data-diff --dbt' command to compare dbt models between environments.
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Learn how to test and validate dbt code changes in Snowflake with data-diff. After building your dbt models with 'dbt run' in development you can run the 'data-diff --dbt' command to compare dbt models between environments.
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Learn how to use dbt-expectations to test dbt sources and models. Avoid alert fatigue by limiting the scope of your tests and using Datafold to validate dbt code changes.
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Learn how to use dbt-expectations to test dbt sources and models. Avoid alert fatigue by limiting the scope of your tests and using Datafold to validate dbt code changes.
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Data quality issues are caused by changes in the code, data and infrastructure. Compare solutions to test code, observe data and monitor infrastructure.
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Data quality issues are caused by changes in the code, data and infrastructure. Compare solutions to test code, observe data and monitor infrastructure.
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Learn how to create dbt Python models in Snowflake, Databricks and BigQuery. dbt Python models are defined as a Python function named model that returns a dataframe.
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Learn how to create dbt Python models in Snowflake, Databricks and BigQuery. dbt Python models are defined as a Python function named model that returns a dataframe.
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Discover dbt Cloud key features and learn what it takes to implement equivalent features for dbt Core.
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Discover dbt Cloud key features and learn what it takes to implement equivalent features for dbt Core.
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Get an overview of Data Quality Meetup #8. With speakers from Maple, Crane Worldwide Logistics, Zingg.ai, Entalytica.com and Datafold. The Meetup included lightning rounds on Improving Data Quality, Data Quality Defenses, Entity Resolution, CI Pipelines and Datafold's launch of the dbt and Data Diff integration.
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Get an overview of Data Quality Meetup #8. With speakers from Maple, Crane Worldwide Logistics, Zingg.ai, Entalytica.com and Datafold. The Meetup included lightning rounds on Improving Data Quality, Data Quality Defenses, Entity Resolution, CI Pipelines and Datafold's launch of the dbt and Data Diff integration.
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Whatever layer of the data stack you focus on, there is inherently one technology that is valuable to all layers: testing. Discover how testing throughout the stack leads not only to stronger proactivity, but also to more productivity.
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Whatever layer of the data stack you focus on, there is inherently one technology that is valuable to all layers: testing. Discover how testing throughout the stack leads not only to stronger proactivity, but also to more productivity.
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Software testing has been a fundamental component of the software development life cycle (SDLC) for the last 40 years. Though the frameworks and methodologies for software testing have changed dramatically in the last four decades, the approach to data quality testing has not seen the same rate of change until recently.Â
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Software testing has been a fundamental component of the software development life cycle (SDLC) for the last 40 years. Though the frameworks and methodologies for software testing have changed dramatically in the last four decades, the approach to data quality testing has not seen the same rate of change until recently.Â
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To set the stage, letâs first discuss what data testing is all about and how itâs different from data observability. First, itâs important to note that Data Testing is part of the development and deployment workflow. The goal of Data Testing is to catch bugs before they hit production. Data Observability, on the other hand, is concerned with the state of your data after it has made it into production. Learn how data testing gets done today and the best practices for automating tests to improve data quality.
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To set the stage, letâs first discuss what data testing is all about and how itâs different from data observability. First, itâs important to note that Data Testing is part of the development and deployment workflow. The goal of Data Testing is to catch bugs before they hit production. Data Observability, on the other hand, is concerned with the state of your data after it has made it into production. Learn how data testing gets done today and the best practices for automating tests to improve data quality.
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Learn where about the 5 places data quality issues originate from and what tools can help you detect and prevent them.
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Learn where about the 5 places data quality issues originate from and what tools can help you detect and prevent them.
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Code review health is something teams should monitor and iterate on over time. Learn the best strategies for data teams to improve their code review process.
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Code review health is something teams should monitor and iterate on over time. Learn the best strategies for data teams to improve their code review process.
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Learn data validation and verification techniques to confirm a successful migration from Redshift to Snowflake.
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Learn data validation and verification techniques to confirm a successful migration from Redshift to Snowflake.
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To deliver high quality data quickly teams need principles to guide how they move, update, and manage data while keeping their stakeholders informed.
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To deliver high quality data quickly teams need principles to guide how they move, update, and manage data while keeping their stakeholders informed.
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Datafold has officially launched its partnership with Snowflake, the Data Cloud company by providing proactive testing solutions to help joint customers trust and depend on their data.Â
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Datafold has officially launched its partnership with Snowflake, the Data Cloud company by providing proactive testing solutions to help joint customers trust and depend on their data.Â
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Many teams create data, many teams consume data, and the data team is responsible for facilitating.
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Many teams create data, many teams consume data, and the data team is responsible for facilitating.
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Get an overview of the Data Quality Meetup #7. With speakers from Virgin Media O2, Convoy, Nixtla, Metaplane, and Doctolib, the event included lightning rounds on data contracts, data observability, real-time data-sharing, testing automation, and time-series forecasting.
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Get an overview of the Data Quality Meetup #7. With speakers from Virgin Media O2, Convoy, Nixtla, Metaplane, and Doctolib, the event included lightning rounds on data contracts, data observability, real-time data-sharing, testing automation, and time-series forecasting.
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In this episode of Monday Morning Data Chat, Gleb, Matt and Joe discuss improving the Modern Data Stack.
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In this episode of Monday Morning Data Chat, Gleb, Matt and Joe discuss improving the Modern Data Stack.
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How to setup and run dbt with airflow on your local machine.
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How to setup and run dbt with airflow on your local machine.
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Data contracts can help us prevent data quality issues by formalizing interactions and handovers between different systems (and teams) handling data.
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Data contracts can help us prevent data quality issues by formalizing interactions and handovers between different systems (and teams) handling data.
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Datafold now seamlessly integrates with Hightouch to show how changes to a dbt model will impact Hightouch models and syncs.Â
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Datafold now seamlessly integrates with Hightouch to show how changes to a dbt model will impact Hightouch models and syncs.Â
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A guide on how to build data systems that do not break.
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A guide on how to build data systems that do not break.
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