Business Intelligence

Advice On Scaling Your Data Pipeline Alongside Your Business with Christian Heinzmann - Episode 61

Summary

Every business needs a pipeline for their critical data, even if it is just pasting into a spreadsheet. As the organization grows and gains more customers, the requirements for that pipeline will change. In this episode Christian Heinzmann, Head of Data Warehousing at Grubhub, discusses the various requirements for data pipelines and how the overall system architecture evolves as more data is being processed. He also covers the changes in how the output of the pipelines are used, how that impacts the expectations for accuracy and availability, and some useful advice on build vs. buy for the components of a data platform.

Preamble

  • 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 Linode. With 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Christian Heinzmann about how data pipelines evolve as your business grows

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by sharing your definition of a data pipeline?
    • At what point in the life of a project or organization should you start thinking about building a pipeline?
  • In the early stages when the scale of the data and business are still small, what are some of the design characteristics that you should be targeting for your pipeline?
    • What metrics/use cases should you be optimizing for at this point?
  • What are some of the indicators that you look for to signal that you are reaching the next order of magnitude in terms of scale?
    • How do the design requirements for a data pipeline change as you reach this stage?
    • What are some of the challenges and complexities that begin to present themselves as you build and run your pipeline at medium scale?
  • What are some of the changes that are necessary as you move to a large scale data pipeline?
  • At each level of scale it is important to minimize the impact of the ETL process on the source systems. What are some strategies that you have employed to avoid degrading the performance of the application systems?
  • In recent years there has been a shift to using data lakes as a staging ground before performing transformations. What are your thoughts on that approach?
  • When performing transformations there is a potential for discarding information or losing fidelity. How have you worked to reduce the impact of this effect?
  • Transformations of the source data can be brittle when the format or volume changes. How do you design the pipeline to be resilient to these types of changes?
  • What are your selection criteria when determining what workflow or ETL engines to use in your pipeline?
    • How has your preference of build vs buy changed at different scales of operation and as new/different projects become available?
  • What are some of the dead ends or edge cases that you have had to deal with in your current role at Grubhub?
  • What are some of the common mistakes or overlooked aspects of building a data pipeline that you have seen?
  • What are your plans for improving your current pipeline at Grubhub?
  • What are some references that you recommend for anyone who is designing a new data platform?

Contact Info

Parting Question

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

Links

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

Self Service Business Intelligence And Data Sharing Using Looker with Daniel Mintz - Episode 55

Summary

Business intelligence is a necessity for any organization that wants to be able to make informed decisions based on the data that they collect. Unfortunately, it is common for different portions of the business to build their reports with different assumptions, leading to conflicting views and poor choices. Looker is a modern tool for building and sharing reports that makes it easy to get everyone on the same page. In this episode Daniel Mintz explains how the product is architected, the features that make it easy for any business user to access and explore their reports, and how you can use it for your organization today.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Daniel Mintz about Looker, a a modern data platform that can serve the data needs of an entire company

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by describing what Looker is and the problem that it is aiming to solve?
    • How do you define business intelligence?
  • How is Looker unique from other approaches to business intelligence in the enterprise?
    • How does it compare to open source platforms for BI?
  • Can you describe the technical infrastructure that supports Looker?
  • Given that you are connecting to the customer’s data store, how do you ensure sufficient security?
  • For someone who is using Looker, what does their workflow look like?
    • How does that change for different user roles (e.g. data engineer vs sales management)
  • What are the scaling factors for Looker, both in terms of volume of data for reporting from, and for user concurrency?
  • What are the most challenging aspects of building a business intelligence tool and company in the modern data ecosystem?
    • What are the portions of the Looker architecture that you would do differently if you were to start over today?
  • What are some of the most interesting or unusual uses of Looker that you have seen?
  • What is in store for the future of Looker?

Contact Info

Parting Question

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

Links

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

Take Control Of Your Web Analytics Using Snowplow With Alexander Dean - Episode 48

Summary

Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • This is your host Tobias Macey and today I’m interviewing Alexander Dean about Snowplow Analytics

Interview

  • Introductions
  • How did you get involved in the area of data engineering and data management?
  • What is Snowplow Analytics and what problem were you trying to solve when you started the company?
  • What is unique about customer event data from an ingestion and processing perspective?
  • Challenges with properly matching up data between sources
  • Data collection is one of the more difficult aspects of an analytics pipeline because of the potential for inconsistency or incorrect information. How is the collection portion of the Snowplow stack designed and how do you validate the correctness of the data?
    • Cleanliness/accuracy
  • What kinds of metrics should be tracked in an ingestion pipeline and how do you monitor them to ensure that everything is operating properly?
  • Can you describe the overall architecture of the ingest pipeline that Snowplow provides?
    • How has that architecture evolved from when you first started?
    • What would you do differently if you were to start over today?
  • Ensuring appropriate use of enrichment sources
  • What have been some of the biggest challenges encountered while building and evolving Snowplow?
  • What are some of the most interesting uses of your platform that you are aware of?

Keep In Touch

Parting Question

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

Links

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

An Agile Approach To Master Data Management with Mark Marinelli - Episode 46

Summary

With the proliferation of data sources to give a more comprehensive view of the information critical to your business it is even more important to have a canonical view of the entities that you care about. Is customer number 342 in your ERP the same as Bob Smith on Twitter? Using master data management to build a data catalog helps you answer these questions reliably and simplify the process of building your business intelligence reports. In this episode the head of product at Tamr, Mark Marinelli, discusses the challenges of building a master data set, why you should have one, and some of the techniques that modern platforms and systems provide for maintaining it.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Mark Marinelli about data mastering for modern platforms

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by establishing a definition of data mastering that we can work from?
    • How does the master data set get used within the overall analytical and processing systems of an organization?
  • What is the traditional workflow for creating a master data set?
    • What has changed in the current landscape of businesses and technology platforms that makes that approach impractical?
    • What are the steps that an organization can take to evolve toward an agile approach to data mastering?
  • At what scale of company or project does it makes sense to start building a master data set?
  • What are the limitations of using ML/AI to merge data sets?
  • What are the limitations of a golden master data set in practice?
    • Are there particular formats of data or types of entities that pose a greater challenge when creating a canonical format for them?
    • Are there specific problem domains that are more likely to benefit from a master data set?
  • Once a golden master has been established, how are changes to that information handled in practice? (e.g. versioning of the data)
  • What storage mechanisms are typically used for managing a master data set?
    • Are there particular security, auditing, or access concerns that engineers should be considering when managing their golden master that goes beyond the rest of their data infrastructure?
    • How do you manage latency issues when trying to reference the same entities from multiple disparate systems?
  • What have you found to be the most common stumbling blocks for a group that is implementing a master data platform?
    • What suggestions do you have to help prevent such a project from being derailed?
  • What resources do you recommend for someone looking to learn more about the theoretical and practical aspects of data mastering for their organization?

Contact Info

Parting Question

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

Links

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

PrestoDB and Starburst Data with Kamil Bajda-Pawlikowski - Episode 32

Summary

Most businesses end up with data in a myriad of places with varying levels of structure. This makes it difficult to gain insights from across departments, projects, or people. Presto is a distributed SQL engine that allows you to tie all of your information together without having to first aggregate it all into a data warehouse. Kamil Bajda-Pawlikowski co-founded Starburst Data to provide support and tooling for Presto, as well as contributing advanced features back to the project. In this episode he describes how Presto is architected, how you can use it for your analytics, and the work that he is doing at Starburst Data.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • Your host is Tobias Macey and today I’m interviewing Kamil Bajda-Pawlikowski about Presto and his experiences with supporting it at Starburst Data

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you start by explaining what Presto is?
    • What are some of the common use cases and deployment patterns for Presto?
  • How does Presto compare to Drill or Impala?
  • What is it about Presto that led you to building a business around it?
  • What are some of the most challenging aspects of running and scaling Presto?
  • For someone who is using the Presto SQL interface, what are some of the considerations that they should keep in mind to avoid writing poorly performing queries?
    • How does Presto represent data for translating between its SQL dialect and the API of the data stores that it interfaces with?
  • What are some cases in which Presto is not the right solution?
  • What types of support have you found to be the most commonly requested?
  • What are some of the types of tooling or improvements that you have made to Presto in your distribution?
    • What are some of the notable changes that your team has contributed upstream to Presto?

Contact Info

Parting Question

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

Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / {CC BY-SA](http://creativecommons.org/licenses/by-sa/3.0/)

Metabase Self Service Business Intelligence with Sameer Al-Sakran - Episode 29

Summary

Business Intelligence software is often cumbersome and requires specialized knowledge of the tools and data to be able to ask and answer questions about the state of the organization. Metabase is a tool built with the goal of making the act of discovering information and asking questions of an organizations data easy and self-service for non-technical users. In this episode the CEO of Metabase, Sameer Al-Sakran, discusses how and why the project got started, the ways that it can be used to build and share useful reports, some of the useful features planned for future releases, and how to get it set up to start using it in your environment.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • For complete visibility into the health of your pipeline, including deployment tracking, and powerful alerting driven by machine-learning, DataDog has got you covered. With their monitoring, metrics, and log collection agent, including extensive integrations and distributed tracing, you’ll have everything you need to find and fix performance bottlenecks in no time. Go to dataengineeringpodcast.com/datadog today to start your free 14 day trial and get a sweet new T-Shirt.
  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • Your host is Tobias Macey and today I’m interviewing Sameer Al-Sakran about Metabase, a free and open source tool for self service business intelligence

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • The current goal for most companies is to be “data driven”. How would you define that concept?
    • How does Metabase assist in that endeavor?
  • What is the ratio of users that take advantage of the GUI query builder as opposed to writing raw SQL?
    • What level of complexity is possible with the query builder?
  • What have you found to be the typical use cases for Metabase in the context of an organization?
  • How do you manage scaling for large or complex queries?
  • What was the motivation for using Clojure as the language for implementing Metabase?
  • What is involved in adding support for a new data source?
  • What are the differentiating features of Metabase that would lead someone to choose it for their organization?
  • What have been the most challenging aspects of building and growing Metabase, both from a technical and business perspective?
  • What do you have planned for the future of Metabase?

Contact Info

Parting Question

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

Links

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