Summary

The promise of streaming data is that it allows you to react to new information as it happens, rather than introducing latency by batching records together. The peril is that building a robust and scalable streaming architecture is always more complicated and error-prone than you think it's going to be. After experiencing this unfortunate reality for themselves, Abhishek Chauhan and Ashish Kumar founded Grainite so that you don't have to suffer the same pain. In this episode they explain why streaming architectures are so challenging, how they have designed Grainite to be robust and scalable, and how you can start using it today to build your streaming data applications without all of the operational headache.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack today to learn more Hey there podcast listener, are you tired of dealing with the headache that is the 'Modern Data Stack'? We feel your pain. It's supposed to make building smarter, faster, and more flexible data infrastructures a breeze. It ends up being anything but that. Setting it up, integrating it, maintaining it—it’s all kind of a nightmare. And let's not even get started on all the extra tools you have to buy to get it to do its thing. But don't worry, there is a better way. TimeXtender takes a holistic approach to data integration that focuses on agility rather than fragmentation. By bringing all the layers of the data stack together, TimeXtender helps you build data solutions up to 10 times faster and saves you 70-80% on costs. If you're fed up with the 'Modern Data Stack', give TimeXtender a try. Head over to dataengineeringpodcast.com/timextender where you can do two things: watch us build a data estate in 15 minutes and start for free today. Join in with the event for the global data community, Data Council Austin. From March 28-30th 2023, they'll play host to hundreds of attendees, 100 top speakers, and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data. As a listener to the Data Engineering Podcast you can get a special discount of 20% off your ticket by using the promo code dataengpod20. Don't miss out on their only event this year! Visit: dataengineeringpodcast.com/data-council today Your host is Tobias Macey and today I'm interviewing Ashish Kumar and Abhishek Chauhan about Grainite, a platform designed to give you a single place to build streaming data applications

Interview

Introduction How did you get involved in the area of data management? Can you describe what Grainite is and the story behind it? What are the personas that you are focused on addressing with Grainite? What are some of the most complex aspects of building streaming data applications in the absence of something like Grainite?

How does Grainite work to reduce that complexity?

What are some of the commonalities that you see in the teams/organizations that find their way to Grainite?

What are some of the higher-order projects that teams are able to build when they are using Grainite as a starting point vs. where they would be spending effort on a fully managed streaming architecture?

Can you describe how Grainite is architected?

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

Wh

Summary Logistics and supply chains are under increased stress and scrutiny in recent years. In order to stay ahead of customer demands, businesses need to be able to react quickly and intelligently to changes, which requires fast and accurate insights into their operations. Pathway is a streaming database engine that embeds artificial intelligence into the storage, with functionality designed to support the spatiotemporal data that is crucial for shipping and logistics. In this episode Adrian Kosowski explains how the Pathway product got started, how its design simplifies the creation of data products that support supply chain operations, and how developers can help to build an ecosystem of applications that allow businesses to accelerate their time to insight.

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 new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show! Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos. Prefect is the modern Dataflow Automation platform for the modern data stack, empowering data practitioners to build, run and monitor robust pipelines at scale. Guided by the principle that the orchestrator shouldn’t get in your way, Prefect is the only tool of its kind to offer the flexibility to write code as workflows. Prefect specializes in glueing together the disparate pieces of a pipeline, and integrating with modern distributed compute libraries to bring power where you need it, when you need it. Trusted by thousands of organizations and supported by over 20,000 community members, Prefect powers over 100MM business critical tasks a month. For more information on Prefect, visit dataengineeringpodcast.com/prefect. Data engineers don’t enjoy writing, maintaining, and modifying ETL pipelines all day, every day. Especially once they realize 90% of all major data sources like Google Analytics, Salesforce, Adwords, Facebook, Spreadsheets, etc., are already available as plug-and-play connectors with reliable, intuitive SaaS solutions. Hevo Data is a highly reliable and intuitive data pipeline platform used by data engineers from 40+ countries to set up and run low-latency ELT pipelines with zero maintenance. Boasting more than 150 out-of-the-box connectors that can be set up in minutes, Hevo also allows you to monitor and control your pipelines. You get: real-time data flow visibility, fail-safe mechanisms, and alerts if anything breaks; preload transformations and auto-schema mapping precisely control how data lands in your destination; models and workflows to transform data for analytics; and reverse-ETL capability to move the transformed data back to your business software to inspire timely action. All of this, plus its transparent pricing and 24*7 live s

Summary Controlling access to a database is a solved problem… right? It can be straightforward for small teams and a small number of storage engines, but once either or both of those start to scale then things quickly become complex and difficult to manage. After years of running across the same issues in numerous companies and even more projects Justin McCarthy built strongDM to solve database access management for everyone. In this episode he explains how the strongDM proxy works to grant and audit access to storage systems and the benefits that it provides to engineers and team leads.

Introduction

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. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Justin McCarthy about StrongDM, a hosted service that simplifies access controls for your data

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining the problem that StrongDM is solving and how the company got started?

What are some of the most common challenges around managing access and authentication for data storage systems? What are some of the most interesting workarounds that you have seen? Which areas of authentication, authorization, and auditing are most commonly overlooked or misunderstood?

Can you describe the architecture of your system?

What strategies have you used to enable interfacing with such a wide variety of storage systems?

What additional capabilities do you provide beyond what is natively available in the underlying systems? What are some of the most difficult aspects of managing varying levels of permission for different roles across the diversity of platforms that you support, given that they each have different capabilities natively? For a customer who is onboarding, what is involved in setting up your platform to integrate with their systems? What are some of the assumptions that you made about your problem domain and market when you first started which have been disproven? How do organizations in different industries react to your product and how do their policies around granting access to data differ? What are some of the most interesting/unexpected/challenging lessons that you have learned in the process of building and growing StrongDM?

Contact Info

LinkedIn @justinm on Twitter

Parting Question

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

Links

StrongDM Authentication Vs. Authorization Hashicorp Vault Configuration Management Chef Puppet SaltStack Ansible Okta SSO (Single Sign On SOC 2 Two Factor Authentication SSH (Secure SHell) RDP

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

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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

Sameer

salsakran on GitHub @sameer_alsakran on Twitter LinkedIn

Metabase

Website @metabase on Twitter metabase on GitHub

Parting Question

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

Links

Expa Metabase Blackjet Hadoop Imeem Maslow’s Hierarchy of Data Needs 2 Sided Marketplace Honeycomb Interview Excel Tableau Go-JEK Clojure React Python Scala JVM Redash How To Lie With Data Stripe Braintree Payments

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

Summary

Building a data pipeline that is reliable and flexible is a difficult task, especially when you have a small team. Astronomer is a platform that lets you skip straight to processing your valuable business data. Ry Walker, the CEO of Astronomer, explains how the company got started, how the platform works, and their commitment to open source.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.dataengineeringpodcast.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers This is your host Tobias Macey and today I’m interviewing Ry Walker, CEO of Astronomer, the platform for data engineering.

Interview

Introduction How did you first get involved in the area of data management? What is Astronomer and how did it get started? Regulatory challenges of processing other people’s data What does your data pipelining architecture look like? What are the most challenging aspects of building a general purpose data management environment? What are some of the most significant sources of technical debt in your platform? Can you share some of the failures that you have encountered while architecting or building your platform and company and how you overcame them? There are certain areas of the overall data engineering workflow that are well defined and have numerous tools to choose from. What are some of the unsolved problems in data management? What are some of the most interesting or unexpected uses of your platform that you are aware of?

Contact Information

Email @rywalker on Twitter

Links

Astronomer Kiss Metrics Segment Marketing tools chart Clickstream HIPAA FERPA PCI Mesos Mesos DC/OS Airflow SSIS Marathon Prometheus Grafana Terraform Kafka Spark ELK Stack React GraphQL PostGreSQL MongoDB Ceph Druid Aries Vault Adapter Pattern Docker Kinesis API Gateway Kong AWS Lambda Flink Redshift NOAA Informatica SnapLogic Meteor

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