Summary Every part of the business relies on data, yet only a small team has the context and expertise to build and maintain workflows and data pipelines to transform, clean, and integrate it. In order for the true value of your data to be realized without burning out your engineers you need a way for everyone to get access to the information they care about. To help make that a more tractable problem Blake Burch co-founded Shipyard. In this episode he explains the utility of a low code solution that lets non engineers create their own self-serve pipelines, how the Shipyard platform is designed to make that possible, and how it allows engineers to create reusable tasks to satisfy the specific needs of the business. This is an interesting conversation about how to make data more accessible and more useful by improving the user experience of the tools that we create.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit dataengineeringpodcast.com/molecula and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt. Your host is Tobias Macey and today I’m interviewing Blake Burch about Shipyard, and his mission to create the easiest way for data teams to launch, monitor, and share resilient pipelines with less engineering

Interview

Introduction How did you get involved in the area of data management? Can you describe what you are building at Shipyard and the story behind it? What are the main goals that you have for Shipyard?

How does it compare to other data orchestration frameworks in the market?

Who are

Summary The data warehouse has become the focal point of the modern data platform. With increased usage of data across businesses, and a diversity of locations and environments where data needs to be managed, the warehouse engine needs to be fast and easy to manage. Yellowbrick is a data warehouse platform that was built from the ground up for speed, and can work across clouds and all the way to the edge. In this episode CTO Mark Cusack explains how the engine is architected, the benefits that speed and predictable pricing has for the organization, and how you can simplify your platform by putting the warehouse close to the data, instead of the other way around.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Mark Cusack about Yellowbrick, a data warehouse designed for distributed clouds

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Yellowbrick is and some of the story behind it? What does the term "distributed cloud" signify and what challenges are associated with it? How would you characterize Yellowbrick’s position in the database/DWH market? How is Yellowbrick architected?

How have the goals and design of the platform changed or evolved over time?

How does Yellowbrick maintain visibility across the different data locations that it is responsible for?

What capabilities does it offer for being able to join across the disparate "clouds"?

What are some data modeling strategies that users should consider when designing their deployment of Yellowbrick? What are some of the capabilities of Yellowbrick that you find most useful or technically interesting? For someone who is adopting Yellowbrick, what is the process for getting it integrated into their data systems? What are the most underutilized, overlooked, or misunderstood features of Yellowbrick? What are the most interesting, innovative, or unexpected ways that you have seen Yellowbrick used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Yellowbrick? When is Yellowbrick the wrong choice? What do you have planned for the future of the product?

Contact Info

LinkedIn @markcusack on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Yellowbrick Teradata Rainstor Distributed Cloud Hybrid Cloud SwimOS

Podcast Episode

K

This IBM® Redpaper Redbooks publication provides guidance about a backup and recovery solution for SAP High-performance Analytic Appliance (HANA) running on IBM Power Systems. This publication provides case studies and how-to procedures that show backup and recovery scenarios. This publication provides information about how to protect data in an SAP HANA environment by using IBM Spectrum® Protect and IBM Spectrum Copy Data Manager. This publication focuses on the data protection solution, which is described through several scenarios. The information in this publication is distributed on an as-is basis without any warranty that is either expressed or implied. Support assistance for the use of this material is limited to situations where IBM Spectrum Scale or IBM Spectrum Protect are supported and entitled, and where the issues are specific to a blueprint implementation. The goal of the publication is to describe the best aspects and options for backup, snapshots, and restore of SAP HANA Multitenant Database Container (MDC) single and multi-tenant installations on IBM Power Systems by using theoretical knowledge, hands-on exercises, and documenting the findings through sample scenarios. This document provides resources about the following processes: Describing how to determine the best option, including SAP Landscape aspects to back up, snapshot, and restore of SAP HANA MDC single and multi-tenant installations based on IBM Spectrum Computing Suite, Red Hat Linux Relax and Recover (ReAR), and other products. Documenting key aspects, such as recovery time objective (RTO) and recovery point objective (RPO), backup impact (load, duration, scheduling), quantitative savings (for example, data deduplication), integration and catalog currency, and tips and tricks that are not covered in the product documentation. Using IBM Cloud® Object Storage and documenting how to use IBM Spectrum Protect to back up to the cloud. SAP HANA 2.0 SPS 05 has this feature that is built in natively. IBM Spectrum Protect for Enterprise Resource Planning (ERP) has this feature too. Documenting Linux ReaR to cover operating system (OS) backup because ReAR is used by most backup products, such as IBM Spectrum Protect and Symantec Endpoint Protection (SEP) to back up OSs. This publication targets technical readers including IT specialists, systems architects, brand specialists, sales teams, and anyone looking for a guide about how to implement the best options for SAP HANA backup and recovery on IBM Power Systems. Moreover, this publication provides documentation to transfer the how-to-skills to the technical teams and solution guidance to the sales team. This publication complements the documentation that is available at IBM Knowledge Center, and it aligns with the educational materials that are provided by IBM Garage™ for Systems Technical Education and Training.

IBM® Power Virtualization Center (IBM® PowerVC™) is an advanced enterprise virtualization management offering for IBM Power Systems. This IBM Redbooks® publication introduces IBM PowerVC and helps you understand its functions, planning, installation, and setup. It also shows how IBM PowerVC can integrate with systems management tools such as Ansible or Terraform and that it also integrates well into a OpenShift container environment. IBM PowerVC Version 2.0.0 supports both large and small deployments, either by managing IBM PowerVM® that is controlled by the Hardware Management Console (HMC), or by IBM PowerVM NovaLink. With this capability, IBM PowerVC can manage IBM AIX®, IBM i, and Linux workloads that run on IBM POWER® hardware. IBM PowerVC is available as a Standard Edition, or as a Private Cloud Edition. IBM PowerVC includes the following features and benefits: Virtual image capture, import, export, deployment, and management Policy-based virtual machine (VM) placement to improve server usage Snapshots and cloning of VMs or volumes for backup or testing purposes Support of advanced storage capabilities such as IBM SVC vdisk mirroring of IBM Global Mirror Management of real-time optimization and VM resilience to increase productivity VM Mobility with placement policies to reduce the burden on IT staff in a simple-to-install and easy-to-use graphical user interface (GUI) Automated Simplified Remote Restart for improved availability of VMs ifor when a host is down Role-based security policies to ensure a secure environment for common tasks The ability to enable an administrator to enable Dynamic Resource Optimization on a schedule IBM PowerVC Private Cloud Edition includes all of the IBM PowerVC Standard Edition features and enhancements: A self-service portal that allows the provisioning of new VMs without direct system administrator intervention. There is an option for policy approvals for the requests that are received from the self-service portal. Pre-built deploy templates that are set up by the cloud administrator that simplify the deployment of VMs by the cloud user. Cloud management policies that simplify management of cloud deployments. Metering data that can be used for chargeback. This publication is for experienced users of IBM PowerVM and other virtualization solutions who want to understand and implement the next generation of enterprise virtualization management for Power Systems. Unless stated otherwise, the content of this publication refers to IBM PowerVC Version 2.0.0.

Summary Machine learning models use vectors as the natural mechanism for representing their internal state. The problem is that in order for the models to integrate with external systems their internal state has to be translated into a lower dimension. To eliminate this impedance mismatch Edo Liberty founded Pinecone to build database that works natively with vectors. In this episode he explains how this technology will allow teams to accelerate the speed of innovation, how vectors make it possible to build more advanced search functionality, and how Pinecone is architected. This is an interesting conversation about how reconsidering the architecture of your systems can unlock impressive new capabilities.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit dataengineeringpodcast.com/molecula and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt. Your host is Tobias Macey and today I’m interviewing Edo Liberty about Pinecone, a vector database for powering machine learning and similarity search

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what Pinecone is and the story behind it? What are some of the contexts where someone would want to perform a similarity search?

What are the considerations that someone should be aware of when deciding between Pinecone and Solr/Lucene for a search oriented use case?

What are some of the other use cases that Pinecone enables? In the absence of Pinecone, what kinds of systems and solutions are people b

Through explosive growth in the past decade, data now drives significant portions of our lives, from crowdsourced restaurant recommendations to AI systems identifying effective medical treatments. Software developers have unprecedented opportunity to build data applications that generate value from massive datasets across use cases such as customer 360, application health and security analytics, the IoT, machine learning, and embedded analytics. With this report, product managers, architects, and engineering teams will learn how to make key technical decisions when building data-intensive applications, including how to implement extensible data pipelines and share data securely. The report includes design considerations for making these decisions and uses the Snowflake Data Cloud to illustrate best practices. This report explores: Why data applications matter: Get an introduction to data applications and some of the most common use cases Evaluating platforms for building data apps: Evaluate modern data platforms to confidently consider the merits of potential solutions Building scalable data applications: Learn design patterns and best practices for storage, compute, and security Handling and processing data: Explore techniques and real-world examples for building data pipelines to support data applications Designing for data sharing: Learn best practices for sharing data in modern data applications

In 'Distributed Data Systems with Azure Databricks', you will explore the capabilities of Microsoft Azure Databricks as a platform for building and managing big data pipelines. Learn how to process, transform, and analyze data at scale while developing expertise in training distributed machine learning models and integrating them into enterprise workflows. What this Book will help me do Design and implement Extract, Transform, Load (ETL) pipelines using Azure Databricks. Conduct distributed training of machine learning models using TensorFlow and Horovod. Integrate Azure Databricks with Azure Data Factory for optimized data pipeline orchestration. Utilize Delta Engine for efficient querying and analysis of data within Delta Lake. Employ Databricks Structured Streaming to manage real-time production-grade data flows. Author(s) None Palacio is an experienced data engineer and cloud computing specialist, with extensive knowledge of the Microsoft Azure platform. With years of practical application of Databricks in enterprise settings, Palacio provides clear, actionable insights through relatable examples. They bring a passion for innovative solutions to the field of big data automation. Who is it for? This book is ideal for data engineers, machine learning engineers, and software developers looking to master Azure Databricks for large-scale data processing and analysis. Readers should have basic familiarity with cloud platforms, understanding of data pipelines, and a foundational grasp of Python and machine learning concepts. It is perfect for those wanting to create scalable and manageable data workflows.

Summary Data governance is a phrase that means many different things to many different people. This is because it is actually a concept that encompasses the entire lifecycle of data, across all of the people in an organization who interact with it. Stijn Christiaens co-founded Collibra with the goal of addressing the wide variety of technological aspects that are necessary to realize such an important and expansive process. In this episode he shares his thoughts on the balance between human and technological processes that are necessary for a well-managed data governance strategy, how Collibra is designed to aid in that endeavor, and his experiences using the platform that his company is building to help power the company. This is an excellent conversation that spans the engineering and philosophical complexities of an important and ever-present aspect of working with data.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management

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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!

RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today.

Your host is Tobias Macey and today I’m interviewing Stijn Christiaens about data governance in the enterprise and how Collibra applies the lessons learned from their customers to their own business

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Collibra and the story behind the company? Wat does "data governance" mean to you, and how does that definition inform your work at Collibra?

How would you characterize the current landscape of "data governance" offerings and Collibra’s position within it?

What are the elements of governance that are often ignored in small/medium businesses but which are essential for the enterprise? (e.g. data stewards, business glossaries, etc.) One of the most important tasks as a data professional is to establish and maintain trust in the information you are curating. What are the biggest obstacles to overcome in that mission? What are some of the data problems that you will only find at large or complex organizations?

How does Collibra help to tame that complexity?

Who are the end users of Collibra within an organization? Can you talk through the workflow and various interactions that your customers have as it relates to the overall flow of data through an organization? Can you describe how the Collibra platform is implemented?

How has the scope and design of the system evolved since you first began working on it?

You are currently leading a team that uses Collibra to manage the operations of the business. What are some of the most notable surprises that you have learned from being your own customer?

What are some of the weak points that you have be

This IBM® Redpaper publication is a comprehensive guide that covers the IBM Power System IC922 (9183-22X) server that uses IBM POWER9™ processor-based technology and supports Linux operating systems (OSs). The objective of this paper is to introduce the system offerings and their capacities and available features. The Power IC922 server is built to deliver powerful computing, scaling efficiency, and storage capacity in a cost-optimized design to meet the evolving data challenges of the artificial intelligence (AI) era. It includes the following features: High throughput and performance for high-value Linux workloads, such as inferencing data or storage-rich workloads, or cloud. Potentially low acquisition cost through system optimization, such as using industry standard memory and warranty. Two IBM POWER9 processor-based single-chip module (SCM) devices that provide high performance with 24, 32, or 40 fully activated cores and a maximum 2 TB of memory. Up to six NVIDIA T4 graphics processing unit (GPU) accelerators. Up to twenty-four 2.5-inch SAS/SATA drives. One dedicated and one shared 1 Gb Intelligent Platform Management Interface (IPMI) port.. This publication is for professionals who want to acquire a better understanding of IBM Power Systems products. The intended audience includes: Clients Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors (ISVs) This paper expands the current set of IBM Power Systems documentation by providing a desktop reference that offers a detailed technical description of the Power IC922 server.

Imagine you are a business user, consultant, or developer about to enter an SAP S/4HANA implementation project. You are well-versed with SAP’s product portfolio and you know that the preferred reporting option in S/4HANA is embedded analytics. But what exactly is embedded analytics? And how can it be implemented? And who can do it: a business user, a functional consultant specialized in financial or logistics processes? Or does a business intelligence expert or a programmer need to be involved? Good questions! This book will answer these questions, one by one. It will also take you on the same journey that the implementation team needs to follow for every reporting requirement that pops up: start with assessing a more standard option and only move on to a less standard option if the requirement cannot be fulfilled. In consecutive chapters, analytical apps delivered by SAP, apps created using Smart Business Services, and Analytical Queries developed either using tiles or in adevelopment environment are explained in detail with practical examples. The book also explains which option is preferred in which situation. The book covers topics such as in-memory computing, cloud, UX, OData, agile development, and more.Author Freek Keijzer writes from the perspective of an implementation consultant, focusing on functionality that has proven itself useful in the field. Practical examples are abundant, ranging from “codeless” to “hardcore coding.” What You Will Learn Know the difference between static reporting and interactive querying on real-time data Understand which options are available for analytics in SAP S/4HANA Understand which option to choose in which situation Know how to implement these options Who This Book is For SAP power users, functional consultants, developers

Summary Data lineage is the common thread that ties together all of your data pipelines, workflows, and systems. In order to get a holistic understanding of your data quality, where errors are occurring, or how a report was constructed you need to track the lineage of the data from beginning to end. The complicating factor is that every framework, platform, and product has its own concepts of how to store, represent, and expose that information. In order to eliminate the wasted effort of building custom integrations every time you want to combine lineage information across systems Julien Le Dem introduced the OpenLineage specification. In this episode he explains his motivations for starting the effort, the far-reaching benefits that it can provide to the industry, and how you can start integrating it into your data platform today. This is an excellent conversation about how competing companies can still find mutual benefit in co-operating on open standards.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. When it comes to serving data for AI and ML projects, do you feel like you have to rebuild the plane while you’re flying it across the ocean? Molecula is an enterprise feature store that operationalizes advanced analytics and AI in a format designed for massive machine-scale projects without having to manage endless one-off information requests. With Molecula, data engineers manage one single feature store that serves the entire organization with millisecond query performance whether in the cloud or at your data center. And since it is implemented as an overlay, Molecula doesn’t disrupt legacy systems. High-growth startups use Molecula’s feature store because of its unprecedented speed, cost savings, and simplified access to all enterprise data. From feature extraction to model training to production, the Molecula feature store provides continuously updated feature access, reuse, and sharing without the need to pre-process data. If you need to deliver unprecedented speed, cost savings, and simplified access to large scale, real-time data, visit dataengineeringpodcast.com/molecula and request a demo. Mention that you’re a Data Engineering Podcast listener, and they’ll send you a free t-shirt. Your host is Tobias Macey and today I’m interviewing Julien Le Dem about Open Lineage, a new standard for structuring metadata to enable interoperability across the ecosystem of data management tools.

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what the Open Lineage project is and the story behind it? What is the current state of t

Summary There is a lot of attention on the database market and cloud data warehouses. While they provide a measure of convenience, they also require you to sacrifice a certain amount of control over your data. If you want to build a warehouse that gives you both control and flexibility then you might consider building on top of the venerable PostgreSQL project. In this episode Thomas Richter and Joshua Drake share their advice on how to build a production ready data warehouse with Postgres.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Thomas Richter and Joshua Drake about using Postgres as your data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you start by establishing a working definition of what constitutes a data warehouse for the purpose of this discussion?

What are the limitations for out-of-the-box Postgres when trying to use it for these workloads?

There are a large and growing number of options for data warehouse style workloads. How would you categorize the different systems and what is PostgreSQL’s position in that ecosystem?

What do you see as the motivating factors for a team or organization to select from among those categories?

Why would someone want to use Postgres as their data warehouse platform rather than using a purpose-built engine? What is the cost/performance equation for Postgres as compared to other data warehouse solutions? For someone who wants to turn Postgres into a data warehouse engine, what are their options?

What are the relative tradeoffs of the different open source and commercial offerings? (e.g. Citus, cstore_fdw, zedstore, Swarm64, Greenplum, etc.)

One of the biggest areas of growth right now is in the "cloud data warehouse" market where storage and compute are decoupled. What are the options for making that possible with Postgres? (e.g. using foreign data wrappers for interacting with data lake storage (S3, HDFS, Alluxio, etc.)) What areas of work are happening in the Postgres community for upcoming releases to make it more easily suited to data warehouse/analytical workloads? What are some of the most interesting, innovative, or unexpected ways that you have seen Postgres used in analytical contexts? What are the most interesting, unexpected, or challenging lessons that you have learned from your own experiences of building analytical systems with Postgres? When is Postgres the wrong choice fo

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Orr Danon CEO at Hailo Technologies. Hailo has developed a breakthrough deep learning microprocessor based on a novel architecture which enables edge devices to run sophisticated deep learning applications that could previously run only on the cloud. Orr has a decade of software and engineering experience from the Israel Defense Forces’ elite intelligence unit. Orr coordinated many of the unit’s largest and most complex interdisciplinary projects, ultimately earning the Israel Defense Award granted by Israel’s president, and the Creative Thinking Award, bestowed by the head of Israel’s military intelligence. Orr holds an M.Sc in Electrical and Electronics Engineering from Tel Aviv University and a B.Sc in Physics from the Hebrew University in Jerusalem. Show Notes 4:28 – Is Edge a hardware solution? 11:45 – Where do you think the fastest growth in the Edge is going to be? 14:01 – What makes your AI chip different? 17:10 – Anything else in your secret sauce that makes you different? 18:28 – If I am a customer what do I do for proof of technology? And do your chips work together at the Edge? 21:35 – What about security? 22:50 – Tell us about the data? 26:40 – Where will you be in 5 years? Hailo website    Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary Building an API for real-time data is a challenging project. Making it robust, scalable, and fast is a full time job. The team at Tinybird wants to make it easy to turn a continuous stream of data into a production ready API or data product. In this episode CEO Jorge Sancha explains how they have architected their system to handle high data throughput and fast response times, and why they have invested heavily in Clickhouse as the core of their platform. This is a great conversation about the challenges of building a maintainable business from a technical and product perspective.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Ascend.io — recognized as a 2021 Gartner Cool Vendor in Enterprise AI Operationalization and Engineering—empowers data teams to to build, scale, and operate declarative data pipelines with 95% less code and zero maintenance. Connect to any data source using Ascend’s new flex code data connectors, rapidly iterate on transformations and send data to any destination in a fraction of the time it traditionally takes—just ask companies like Harry’s, HNI, and Mayvenn. Sound exciting? Come join the team! We’re hiring data engineers, so head on over to dataengineeringpodcast.com/ascend and check out our careers page to learn more. Your host is Tobias Macey and today I’m interviewing Jorge Sancha about Tinybird, a platform to easily build analytical APIs for real-time data

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Tinybird and the story behind it? What are some of the types of use cases that your customers are focused on? What are the areas of complexity that come up when building analytical APIs that are often overlooked when first designing a system to operate on and expose real-time data?

What are the supporting systems that are necessary and useful for operating this kind of system which contribute to the overall time and engineering cost beyond the baseline functionality?

How is the Tinybird platform architected?

How have the goals and implementation of Tinybird changed or evolved since you first began building it?

What was your criteria for selecting the core building block of your platform, and how did that lead to your choice to build on top of Clickhouse? What are some of the sharp edges that you have run into while operating Clickhouse?

What are some of the custom tools or systems that you have built to help deal with them?

What are some of the performance challenges that an API built with Tinybird might run into?

What are the considerations that users should be

A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. About the Technology Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task. About the Book Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. You’ll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. Part reference and part tutorial, this practical guide covers every aspect of the directed acyclic graphs (DAGs) that power Airflow, and how to customize them for your pipeline’s needs. What's Inside Build, test, and deploy Airflow pipelines as DAGs Automate moving and transforming data Analyze historical datasets using backfilling Develop custom components Set up Airflow in production environments About the Reader For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the Authors Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Quotes An Airflow bible. Useful for all kinds of users, from novice to expert. - Rambabu Posa, Sai Aashika Consultancy An easy-to-follow exploration of the benefits of orchestrating your data pipeline jobs with Airflow. - Daniel Lamblin, Coupang The one reference you need to create, author, schedule, and monitor workflows with Apache Airflow. Clear recommendation. - Thorsten Weber, bbv Software Services AG By far the best resource for Airflow. - Jonathan Wood, LexisNexis

Summary 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.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Jean-Yves Stephan about Data Mechanics, a cloud-native Spark platform for data engineers

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of what you are building at Data Mechanics and the story behind it? What are the operational characteristics of Spark that make it difficult to run in a cloud-optimized environment? How do you handle retries, state redistribution, etc. when instances get pre-empted during the middle of a job execution?

What are some of the tactics that you have found useful when designing jobs to make them more resilient to interruptions?

What are the customizations that you have had to make to Spark itself? What are some of the supporting tools that you have built to allow for running Spark in a Kubernetes environment? How is the Data Mechanics platform implemented?

How have the goals and design of the platform changed or evolved since you first began working on it?

How does running Spark in a container/Kubernetes environment change the ways that you and your customers think about how and where to use it?

How does it impact the development workflow for data engineers and data scientists?

What are some of the most interesting, unexpected, or challenging lessons that you have learned while building the Data Mechanics product? When is Spark/Data Mechanics the wrong choice? What do you have planned for the future of the platform?

Contact Info

LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Data Mechanics Databricks Stanford Andrew Ng Mining Massive Datasets Spark Kubernetes Spot Instances Infiniband Data Mechanics Spark Container Image Delight – Spark monitoring utility Terraform Blue/Green Deployment Spark Operator for Kubernetes JupyterHub Jupyter Enterprise Gateway

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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Point and click your way to performing statistics! Many people are intimidated by learning statistics, but A Gentle Introduction to Statistics Using SAS is here to help. Whether you need to perform statistical analysis for a project or, perhaps, for a course in education, psychology, sociology, economics, or any other field that requires basic statistical skills, this book teaches the fundamentals of statistics, from designing your experiment through calculating logistic regressions. Serving as an introduction to many common statistical tests and principles, it explains concepts in an intuitive way with little math and very few formulas. The book is full of examples demonstrating the use of SAS Studio’s easy point-and-click interface accessed with SAS OnDemand for Academics, an online delivery platform for teaching and learning statistical analysis that provides free access to SAS software via the cloud. Studio in the Cloud Topics included in this book are: How to access SAS OnDemand for Academics Descriptive statistics One-sample tests T tests (for independent or paired samples) One-way analysis of variance (ANOVA) N-way ANOVA Correlation analysis Simple and multiple linear regression Binary logistic regression Categorical data, including two-way tables and chi-square Power and sample size calculations Questions are provided to test your knowledge and practice your skills.

Summary The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. More than just a collection of tools, there are a number of organizational and conceptual changes that a proper DataOps approach depends on. In this episode Kevin Stumpf, CTO of Tecton, Maxime Beauchemin, CEO of Preset, and Lior Gavish, CTO of Monte Carlo, discuss the grand vision and present realities of DataOps. They explain how to think about your data systems in a holistic and maintainable fashion, the security challenges that threaten to derail your efforts, and the power of using metadata as the foundation of everything that you do. If you are wondering how to get control of your data platforms and bring all of your stakeholders onto the same page then this conversation is for you.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing Max Beauchemin, Lior Gavish, and Kevin Stumpf about the real world challenges of embracing DataOps practices and systems, and how to keep things secure as you scale

Interview

Introduction How did you get involved in the area of data management? Before we get started, can you each give your definition of what "DataOps" means to you?

How does this differ from "business as usual" in the data industry? What are some of the things that DataOps isn’t (despite what marketers might say)?

What are the biggest difficulties that you have faced in going from concept to production with a workflow or system intended to power self-serve access to other membe

This IBM® Redbooks® publication covers IBM TS7700 R5.1. The IBM TS7700 is part of a family of IBM Enterprise tape products. This book is intended for system architects and storage administrators who want to integrate their storage systems for optimal operation. Building on over 20 years of virtual tape experience, the TS7770 supports the ability to store virtual tape volumes in an object store. The TS7700 supported off loading to physical tape for over two decades. Off loading to physical tape behind a TS7700 is utilized by hundreds of organizations around the world. By using the same hierarchical storage techniques, the TS7700 (TS7770 and TS7760) can also off load to object storage. Because object storage is cloud-based and accessible from different regions, the TS7700 Cloud Storage Tier support essentially allows the cloud to be an extension of the grid. As of this writing, the TS7700C supports the ability to off load to IBM Cloud® Object Storage and Amazon S3. This publication explains features and concepts that are specific to the IBM TS7700 as of release R5.1. The R5.1 microcode level provides IBM TS7700 Cloud Storage Tier enhancements, IBM DS8000® Object Storage enhancements, Management Interface dual control security, and other smaller enhancements. The R5.1 microcode level can be installed on the IBM TS7770 and IBM TS7760 models only. TS7700 provides tape virtualization for the IBM z environment. Tape virtualization can help satisfy the following requirements in a data processing environment: Improved reliability and resiliency Reduction in the time that is needed for the backup and restore process Reduction of services downtime that is caused by physical tape drive and library outages Reduction in cost, time, and complexity by moving primary workloads to virtual tape Increased efficient procedures for managing daily batch, backup, recall, and restore processing On-premises and off-premises object store cloud storage support as an alternative to physical tape for archive and disaster recovery New and existing capabilities of the TS7700 5.1 include the following highlights: Eight-way Grid Cloud, which consists of up to three generations of TS7700 Synchronous and asynchronous replication Full AES256 encryption for replication data that is in-flight and at-rest Tight integration with IBM Z and DFSMS policy management Optional target for DS8000 Transparent Cloud Tier using DFSMS DS8000 Object Store AES256 in-flight encryption and compression Optional Cloud Storage Tier support for archive and disaster recovery 16 Gb IBM FICON® throughput up to 5 GBps per TS7700 cluster IBM Z hosts view up to 3,968 common devices per TS7700 grid Grid access to all data independent of where it exists TS7770 Cache On-demand feature that is based capacity licensing TS7770 support of SSD within the VED server The TS7700T writes data by policy to physical tape through attachment to high-capacity, high-performance IBM TS1150, and IBM TS1140 tape drives that are installed in an IBM TS4500 or TS3500 tape library. The TS7770 models are based on high-performance and redundant IBM POWER9™ technology. They provide improved performance for most IBM Z tape workloads when compared to the previous generations of IBM TS7700.

This IBM® Redbooks® publication introduces the latest member of the IBM Z® platform, the IBM z15™. It includes information about the Z environment and how it helps integrate data and transactions more securely. It also provides insight for faster and more accurate business decisions. The z15 is a state-of-the-art data and transaction system that delivers advanced capabilities, which are vital to any digital transformation. The z15 is designed for enhanced modularity, and occupies an industry-standard footprint. It is offered as a single air-cooled 19-inch frame called the z15 T02, or as a multi-frame (1 to 4 19-inch frames) called the z15 T01. Both z15 models excel at the following tasks:: Using hybrid multicloud integration services Securing and protecting data with encryption everywhere Providing resilience with key to zero downtime Transforming a transactional platform into a data powerhouse Getting more out of the platform with operational analytics Accelerating digital transformation with agile service delivery Revolutionizing business processes Blending open source and IBM Z technologies This book explains how this system uses innovations and traditional Z strengths to satisfy growing demand for cloud, analytics, and open source technologies. With the z15 as the base, applications can run in a trusted, reliable, and secure environment that improves operations and lessens business risk.