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In this episode he shares his thoughts on the strategic and tactical elements of moving your work as a data professional from being task-oriented to being product-oriented and the long term improvements in your productivity that it provides. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you're ready to build your next pipeline, or want to test out the projects you hear about on the show, you'll need somewhere to deploy it, so check out our friends at Linode. or any other destination you choose.
While data warehouses are still in use, they are limited in use-cases as they only support structured data. Datalakes add support for semi-structured and unstructured data, and data lakehouses add further flexibility with better governance in a true hybrid solution built from the ground-up. Book a Demo!
In this episode CEO and founder Salma Bakouk shares her views on the causes and impacts of "data entropy" and how you can tame it before it leads to failures. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.
Using the metaphor of a museum curator carefully managing the precious resources on display and in the vaults, he discusses the various layers of an enterprise data strategy. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science.
In this episode she shares the story behind the project, the details of how it is implemented, and how you can use it for your own data projects. RudderStack helps you build a customer data platform on your warehouse or datalake. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.
With Snowflake, security teams don’t have to work around these data retention windows. Instead, all data is always accessible for analysis, which simplifies cost planning and the datamanagement strategy. A security datalake eliminates data silos by removing limits on ingest and retention.
Datalakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a datalake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.
In our previous post, The Pros and Cons of Leading DataManagement and Storage Solutions , we untangled the differences among datalakes, data warehouses, data lakehouses, data hubs, and data operating systems. What factors are most important when building a datamanagement ecosystem?
In this episode Arjun Narayan explains how the technical barriers to adopting real-time data in your analytics and applications have become surmountable by organizations of all sizes. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows.
In our previous post, The Pros and Cons of Leading DataManagement and Storage Solutions , we untangled the differences among datalakes, data warehouses, data lakehouses, data hubs, and data operating systems. What factors are most important when building a datamanagement ecosystem?
In our previous post, The Pros and Cons of Leading DataManagement and Storage Solutions , we untangled the differences among datalakes, data warehouses, data lakehouses, data hubs, and data operating systems. What factors are most important when building a datamanagement ecosystem?
Summary One of the most critical aspects of software projects is managing its data. Managing the operational concerns for your database can be complex and expensive, especially if you need to scale to large volumes of data, high traffic, or geographically distributed usage. or any other destination you choose.
If you are starting down the path of implementing a data governance strategy then this episode will provide a great overview of what is involved. If you hand a book to a new data engineer, what wisdom would you add to it? What is data governance? If you hand a book to a new data engineer, what wisdom would you add to it?
He also discusses the improvements that have been incorporated into FeatureBase to simplify integration with the rest of your data stack, and the SQL interface that was added to make working with the product easier. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. or any other destination you choose.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you're ready to build your next pipeline, or want to test out the projects you hear about on the show, you'll need somewhere to deploy it, so check out our friends at Linode. or any other destination you choose.
Key Takeaways Data Fabric is a modern data architecture that facilitates seamless data access, sharing, and management across an organization. Datamanagement recommendations and data products emerge dynamically from the fabric through automation, activation, and AI/ML analysis of metadata.
Datalakes, data warehouses, data hubs, data lakehouses, and data operating systems are datamanagement and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.
Datalakes, data warehouses, data hubs, data lakehouses, and data operating systems are datamanagement and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.
Datalakes, data warehouses, data hubs, data lakehouses, and data operating systems are datamanagement and storage solutions designed to meet different needs in data analytics, integration, and processing. This feature allows for a more flexible exploration of data.
They also explain some of the types of data that you can use with Chaos Search, how to load it into S3, and when you might want to choose it over Amazon Athena for our serverless data analysis. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science.
This is a great episode to listen to for ideas on how to organize a data engineering organization. Preamble Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode.
In this episode he shares his experiences working with organizations to adopt analytics engineering patterns and the ways that Optimus and dbt were combined to let data analysts deliver insights without the roadblocks of complex pipeline management. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.
Delivering Use Cases through Chained Data Products Predictive Maintenance : Chaining data products such as IoT sensors, datalakes, and predictive analytics models can enable organizations to implement predictive maintenance use cases.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.
He describes how the platform is architected, the challenges related to selling cloud technologies into enterprise organizations, and how you can adopt Matillion for your own workflows to reduce the maintenance burden of data integration workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.
Summary As more organizations are gaining experience with datamanagement and incorporating analytics into their decision making, their next move is to adopt machine learning. Monte Carlo’s end-to-end Data Observability Platform monitors and alerts for data issues across your data warehouses, datalakes, ETL, and business intelligence.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.
It was interesting to learn about some of the custom data types and performance optimizations that are included. Contact Info Robert LinkedIn hodgesrm on GitHub Alexander alex-zaitsev on GitHub LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for datamanagement today?
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. Missing data?
This method is advantageous when dealing with structured data that requires pre-processing before storage. Conversely, in an ELT-based architecture, data is initially loaded into storage systems such as datalakes in its raw form. Would the data be stored on cloud or on-premises?’
Delivering Use Cases through Chained Data Products Predictive Maintenance : Chaining data products such as IoT sensors, datalakes, and predictive analytics models can enable organizations to implement predictive maintenance use cases. Schedule a demo PDF VERSION PDF VERSION Schedule a demo
Delivering Use Cases through Chained Data Products Predictive Maintenance : Chaining data products such as IoT sensors, datalakes, and predictive analytics models can enable organizations to implement predictive maintenance use cases.
Summit Essentials Date & Location The Gartner Data & AI Summit takes place May 12-15th, 2025 in London, England. This year, the event will uncover the latest in datamanagement, data trends, governance, and data architecture to deliver value for the future.
What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, datamanagement , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a datalake used to host large amounts of raw data.
Understands how different pieces of data (books) relate to each other, helping users find related information. Keeps the library orderly, adjusting as new data arrives. Tracks key performance indicators to understand the use and value of the data. Manages information about data access and monitors data quality.
For today’s Chief Data Officers (CDOs) and data teams, the struggle is real. We’re drowning in data yet thirsting for actionable insights. We need a new approach, a paradigm shift that delivers data with the agility and efficiency of a speedboat – enter Data Products.
Because DataOS connects to everything in the data stack, no data goes unnoticed. Leaders Can Easily Bring the CDP Under Federated Governance CDPs ingest data through both governed and ungoverned channels. To find out more about how DataOS can transform your CDP, schedule a quick call with one of our data experts.
This fanfare turned out to be justified as Slootman, co-founder Benoit Dageville, and Christian Kleinerman took the stage to reveal a series of announcements that promise to disrupt the datamanagement landscape – and beyond. We’ve written previously about how building an external data product is hard. That story?
This technical approach is essential for maintaining data consistency and accuracy across the pipeline. Self-Service Management Modern data pipelines facilitate seamless integration between a wide range of tools, including data integration platforms, data warehouses, datalakes, and programming languages.
The bad news is, integrating data can become a tedious task, especially when done manually. Luckily, there are various data integration tools that support automation and provide a unified data view for more efficient datamanagement. Data integration process. So, why is data integration such a big deal?
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