Cloud

The View From The Lakehouse Of Architectural Patterns For Your Data Platform - Episode 304

The ecosystem for data tools has been going through rapid and constant evolution over the past several years. These technological shifts have brought about corresponding changes in data and platform architectures for managing data and analytical workflows. In this episode Colleen Tartow shares her insights into the motivating factors and benefits of the most prominent patterns that are in the popular narrative; data mesh and the modern data stack. She also discusses her views on the role of the data lakehouse as a building block for these architectures and the ongoing influence that it will have as the technology matures.

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Bringing The Modern Data Stack To Everyone With Y42 - Episode 295

Cloud services have made highly scalable and performant data platforms economical and manageable for data teams. However, they are still challenging to work with and manage for anyone who isn’t in a technical role. Hung Dang understood the need to make data more accessible to the entire organization and created Y42 as a better user experience on top of the “modern data stack”. In this episode he shares how he designed the platform to support the full spectrum of technical expertise in an organization and the interesting engineering challenges involved.

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Data Cloud Cost Optimization With Bluesky Data - Episode 293

The latest generation of data warehouse platforms have brought unprecedented operational simplicity and effectively infinite scale. Along with those benefits, they have also introduced a new consumption model that can lead to incredibly expensive bills at the end of the month. In order to ensure that you can explore and analyze your data without spending money on inefficient queries Mingsheng Hong and Zheng Shao created Bluesky Data. In this episode they explain how their platform optimizes your Snowflake warehouses to reduce cost, as well as identifying improvements that you can make in your queries to reduce their contribution to your bill.

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Cloud Native Data Orchestration For Machine Learning And Data Engineering With Flyte - Episode 291

Machine learning has become a meaningful target for data applications, bringing with it an increase in the complexity of orchestrating the entire data flow. Flyte is a project that was started at Lyft to address their internal needs for machine learning and integrated closely with Kubernetes as the execution manager. In this episode Ketan Umare and Haytham Abuelfutuh share the story of the Flyte project and how their work at Union is focused on supporting and scaling the code and community that has made Flyte successful.

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Building A Data Governance Bridge Between Cloud And Datacenters For The Enterprise At Privacera - Episode 275

Data governance is a practice that requires a high degree of flexibility and collaboration at the organizational and technical levels. The growing prominence of cloud and hybrid environments in data management adds additional stress to an already complex endeavor. Privacera is an enterprise grade solution for cloud and hybrid data governance built on top of the robust and battle tested Apache Ranger project. In this episode Balaji Ganesan shares how his experiences building and maintaining Ranger in previous roles helped him understand the needs of organizations and engineers as they define and evolve their data governance policies and practices.

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Exploring The Design And Benefits Of The Modern Data Stack - Episode 203

We have been building platforms and workflows to store, process, and analyze data since the earliest days of computing. Over that time there have been countless architectures, patterns, and “best practices” to make that task manageable. With the growing popularity of cloud services a new pattern has emerged and been dubbed the “Modern Data Stack”. In this episode members of the GoDataDriven team, Guillermo Sanchez, Bram Ochsendorf, and Juan Perafan, explain the combinations of services that comprise this architecture, share their experiences working with clients to employ the stack, and the benefits of bringing engineers and business users together with data.

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Stick All Of Your Systems And Data Together With SaaSGlue As Your Workflow Manager - Episode 201

At the core of every data workflow is an orchestration engine (or several). Deploying, managing, and scaling that orchestration can consume a large fraction of a data team’s energy so it is important to pick something that provides the power and flexibility that you need. SaaSGlue is a managed service that lets you connect all of your systems, across clouds and physical infrastructure, and spanning all of your programming languages. In this episode Bart and Rich Wood explain how SaaSGlue is architected to allow for a high degree of flexibility in usage and deployment, their experience building a business with family, and how you can get started using it today. This is a fascinating platform with an endless set of use cases and a great team of people behind it.

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Taking A Tour Of The Google Cloud Platform For Data And Analytics - Episode 194

Google pioneered an impressive number of the architectural underpinnings of the broader big data ecosystem. Now they offer the technologies that they run internally to external users of their cloud platform. In this episode Lak Lakshmanan enumerates the variety of services that are available for building your various data processing and analytical systems. He shares some of the common patterns for building pipelines to power business intelligence dashboards, machine learning applications, and data warehouses. If you’ve ever been overwhelmed or confused by the array of services available in the Google Cloud Platform then this episode is for you.

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Making Spark Cloud Native At Data Mechanics - Episode 184

Spark is one of the most well-known frameworks for data processing, whether for batch or streaming, ETL or ML, and at any scale. Because of its popularity it has been deployed on every kind of platform you can think of. In this episode Jean-Yves Stephan shares the work that he is doing at Data Mechanics to make it sing on Kubernetes. He explains how operating in a cloud-native context simplifies some aspects of running the system while complicating others, how it simplifies the development and experimentation cycle, and how you can get a head start using their pre-built Spark container. This is a great conversation for understanding how new ways of operating systems can have broader impacts on how they are being used.

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Cloud Native Data Security As Code With Cyral - Episode 156

One of the most challenging aspects of building a data platform has nothing to do with pipelines and transformations. If you are putting your workflows into production, then you need to consider how you are going to implement data security, including access controls and auditing. Different databases and storage systems all have their own method of restricting access, and they are not all compatible with each other. In order to simplify the process of securing your data in the Cloud Manav Mital created Cyral to provide a way of enforcing security as code. In this episode he explains how the system is architected, how it can help you enforce compliance, and what is involved in getting it integrated with your existing systems. This was a good conversation about an aspect of data management that is too often left as an afterthought.

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