The rise of machine learning has placed a premium on finding new sources of data to fuel predictive models. But acquiring external data is often expensive and many data sets are rife with errors and difficult to combine with internal data. But that’s going to change in 2020.

To help us understand the scale, scope, and dimensions of emerging data marketplaces is Justin Langseth, one of the visionaries in our space. Justin is a VP at Snowflake responsible for the Snowflake Data Exchange.  Prior to Snowflake, Justin was the technical founder and CEO/CTO of 5 data technology startups: Claraview (sold to Teradata), Zoomdata (sold to Logi Analytics), Clarabridge, Strategy.com, and Augaroo. He has 25 years of experience in business intelligence, natural language processing, big data, and AI.

Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditional data warehouse engines, the features that allow you to scale your usage dynamically, and how it allows for a shift in your workflow from ETL to ELT. If you are evaluating your options for building or migrating a data platform, then this is definitely worth a listen.

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 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. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media and the Python Software Foundation. Upcoming events include the Software Architecture Conference in NYC and PyCOn US in Pittsburgh. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Kent Graziano about SnowflakeDB, the cloud-native data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what SnowflakeDB is for anyone who isn’t familiar with it?

How does it compare to the other available platforms for data warehousing? How does it differ from traditional data warehouses?

How does the performance and flexibility affect the data modeling requirements?

Snowflake is one of the data stores that is enabling the shift from an ETL to an ELT workflow. What are the features that allow for that approach and what are some of the challenges that it introduces? Can you describe how the platform is architected and some of the ways that it has evolved as it has grown in popularity?

What are some of the current limitations that you are struggling with?

For someone getting started with Snowflake what is involved with loading data into the platform?

What is their workflow for allocating and scaling compute capacity and running anlyses?

One of the interesting features enabled by your architecture is data sharing. What are some of the most interesting or unexpected uses of that capability that you have seen? What are some other features or use cases for Snowflake that are not as well known or publicized which you think users should know about? When is SnowflakeDB the wrong choice? What are some of the plans for the future of SnowflakeDB?

Contact Info

LinkedIn Website @KentGraziano on Twitter

Parting Question

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

Links

SnowflakeDB

Free Trial Stack Overflow

Data Warehouse Oracle DB MPP == Massively Parallel Processing Shared Nothing Architecture Multi-Cluster Shared Data Architecture Google BigQuery AWS Redshift AWS Redshift Spectrum Presto

Podcast Episode

SnowflakeDB Semi-Structured Data Types Hive ACID == Atomicity, Consistency, Isolation, Durability 3rd Normal Form Data Vault Modeling Dimensional Modeling JSON AVRO Parquet SnowflakeDB Virtual Warehouses CRM == Customer Relationship Management Master Data Management

Podcast Episode

FoundationDB

Podcast Episode

Apache Spark

Podcast Episode

SSIS == SQL Server Integration Services Talend Informatica Fivetran

Podcast Episode

Matillion Apache Kafka Snowpipe Snowflake Data Exchange OLTP == Online Transaction Processing GeoJSON Snowflake Documentation SnowAlert Splunk Data Catalog

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

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Summary The scale and complexity of the systems that we build to satisfy business requirements is increasing as the available tools become more sophisticated. In order to bridge the gap between legacy infrastructure and evolving use cases it is necessary to create a unifying set of components. In this episode Dipti Borkar explains how the emerging category of data orchestration tools fills this need, some of the existing projects that fit in this space, and some of the ways that they can work together to simplify projects such as cloud migration and hybrid cloud environments. It is always useful to get a broad view of new trends in the industry and this was a helpful perspective on the need to provide mechanisms to decouple physical storage from computing capacity.

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 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. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Datacoral helps data engineers build and manage the flow of data pipelines without having to manage any infrastructure, meaning you can spend your time invested in data transformations and business needs, rather than pipeline maintenance. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! and Facebook, scaling from terabytes to petabytes of analytic data. He started Datacoral with the goal to make SQL the universal data programming language. Visit dataengineeringpodcast.com/datacoral today to find out more. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, Corinium Global Intelligence, Alluxio, and Data Council. Upcoming events include the combined events of the Data Architecture Summit and Graphorum, the Data Orchestration Summit, and Data Council in NYC. Go to dataengineeringpodcast.com/conferences to learn more about these and other events, and take advantage of our partner discounts to save money when you register today. Your host is Tobias Macey and today I’m interviewing Dipti Borkark about data orchestration and how it helps in migrating data workloads to the cloud

Interview

Introduction How did you get involved in the area of data management? Can you start by describing what you mean by the term "Data Orchestration"?

How does it compare to the concept of "Data Virtualization"? What are some of the tools and platforms that fit under that umbrella?

What are some of the motivations for organizations to use the cloud for their data oriented workloads?

What are they giving up by using cloud resources in place of on-premises compute?

For businesses that have invested heavily in their own datacenters, what are some ways that they can begin to replicate some of the benefits of cloud environments? What are some of the common patterns for cloud migration projects and what challenges do they present?

Do you have advice on useful metrics to track for determining project completion or success criteria?

How do businesses approach employee education for designing and implementing effective systems for achieving their migration goals? Can you talk through some of the ways that different data orchestration tools can be composed together for a cloud migration effort?

What are some of the common pain points that organizations encounter when working on hybrid implementations?

What are some of the missing pieces in the data orchestration landscape?

Are there any efforts that you are aware of that are aiming to fill those gaps?

Where is the data orchestration market heading, and what are some industry trends that are driving it?

What projects are you most interested in or excited by?

For someone who wants to learn more about data orchestration and the benefits the technologies can provide, what are some resources that you would recommend?

Contact Info

LinkedIn @dborkar on Twitter

Parting Question

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

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Alluxio

Podcast Episode

UC San Diego Couchbase Presto

Podcast Episode

Spark SQL Data Orchestration Data Virtualization PyTorch

Podcast.init Episode

Rook storage orchestration PySpark MinIO

Podcast Episode

Kubernetes Openstack Hadoop HDFS Parquet Files

Podcast Episode

ORC Files Hive Metastore Iceberg Table Format

Podcast Episode

Data Orchestration Summit Star Schema Snowflake Schema Data Warehouse Data Lake Teradata

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

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Summary In recent years the traditional approach to building data warehouses has shifted from transforming records before loading, to transforming them afterwards. As a result, the tooling for those transformations needs to be reimagined. The data build tool (dbt) is designed to bring battle tested engineering practices to your analytics pipelines. By providing an opinionated set of best practices it simplifies collaboration and boosts confidence in your data teams. In this episode Drew Banin, creator of dbt, explains how it got started, how it is designed, and how you can start using it today to create reliable and well-tested reports in your favorite data warehouse.

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 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. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. 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 and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Drew Banin about DBT, the Data Build Tool, a toolkit for building analytics the way that developers build applications

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what DBT is and your motivation for creating it? Where does it fit in the overall landscape of data tools and the lifecycle of data in an analytics pipeline? Can you talk through the workflow for someone using DBT? One of the useful features of DBT for stability of analytics is the ability to write and execute tests. Can you explain how those are implemented? The packaging capabilities are beneficial for enabling collaboration. Can you talk through how the packaging system is implemented?

Are these packages driven by Fishtown Analytics or the dbt community?

What are the limitations of modeling everything as a SELECT statement? Making SQL code reusable is notoriously difficult. How does the Jinja templating of DBT address this issue and what are the shortcomings?

What are your thoughts on higher level approaches to SQL that compile down to the specific statements?

Can you explain how DBT is implemented and how the design has evolved since you first began working on it? What are some of the features of DBT that are often overlooked which you find particularly useful? What are some of the most interesting/unexpected/innovative ways that you have seen DBT used? What are the additional features that the commercial version of DBT provides? What are some of the most useful or challenging lessons that you have learned in the process of building and maintaining DBT? When is it the wrong choice? What do you have planned for the future of DBT?

Contact Info

Email @drebanin on Twitter drebanin on GitHub

Parting Question

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

Links

DBT Fishtown Analytics 8Tracks Internet Radio Redshift Magento Stitch Data Fivetran Airflow Business Intelligence Jinja template language BigQuery Snowflake Version Control Git Continuous Integration Test Driven Development Snowplow Analytics

Podcast Episode

dbt-utils We Can Do Better Than SQL blog post from EdgeDB EdgeDB Looker LookML

Podcast Interview

Presto DB

Podcast Interview

Spark SQL Hive Azure SQL Data Warehouse Data Warehouse Data Lake Data Council Conference Slowly Changing Dimensions dbt Archival Mode Analytics Periscope BI dbt docs dbt repository

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

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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

LinkedIn

Parting Question

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

Links

Looker Upworthy MoveOn.org LookML SQL Business Intelligence Data Warehouse Linux Hadoop BigQuery Snowflake Redshift DB2 PostGres ETL (Extract, Transform, Load) ELT (Extract, Load, Transform) Airflow Luigi NiFi Data Curation Episode Presto Hive Athena DRY (Don’t Repeat Yourself) Looker Action Hub Salesforce Marketo Twilio Netscape Navigator Dynamic Pricing Survival Analysis DevOps BigQuery ML Snowflake Data Sharehouse

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

Tell me about a time you produced an amazing analysis. Please provide your response in the form of a Jupyter notebook that uses Python or R (or both!) to pull words from a corpus that contains all words in the OED stored in a BigQuery table. I mean, that's a fair question to ask, right? No? Well, what questions and techniques are effective for assessing an analyst's likelihood of succeeding in your organization? How should those techniques differ when looking for a technical analyst as opposed to a more business-oriented one? On this episode of the show -- recorded while our recording service clearly thought it was in a job interview that it needed to deliberately tank -- Simon Rumble from Snowflake Analytics joined the gang to share ideas on the topic. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Summary

Building an ETL pipeline is a common need across businesses and industries. It’s easy to get one started but difficult to manage as new requirements are added and greater scalability becomes necessary. Rather than duplicating the efforts of other engineers it might be best to use a hosted service to handle the plumbing so that you can focus on the parts that actually matter for your business. In this episode CTO and co-founder of Alooma, Yair Weinberger, explains how the platform addresses the common needs of data collection, manipulation, and storage while allowing for flexible processing. He describes the motivation for starting the company, how their infrastructure is architected, and the challenges of supporting multi-tenancy and a wide variety of integrations.

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 Yair Weinberger about Alooma, a company providing data pipelines as a service

Interview

Introduction How did you get involved in the area of data management? What is Alooma and what is the origin story? How is the Alooma platform architected?

I want to go into stream VS batch here What are the most challenging components to scale?

How do you manage the underlying infrastructure to support your SLA of 5 nines? What are some of the complexities introduced by processing data from multiple customers with various compliance requirements?

How do you sandbox user’s processing code to avoid security exploits?

What are some of the potential pitfalls for automatic schema management in the target database? Given the large number of integrations, how do you maintain the

What are some challenges when creating integrations, isn’t it simply conforming with an external API?

For someone getting started with Alooma what does the workflow look like? What are some of the most challenging aspects of building and maintaining Alooma? What are your plans for the future of Alooma?

Contact Info

LinkedIn @yairwein on Twitter

Parting Question

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

Links

Alooma Convert Media Data Integration ESB (Enterprise Service Bus) Tibco Mulesoft ETL (Extract, Transform, Load) Informatica Microsoft SSIS OLAP Cube S3 Azure Cloud Storage Snowflake DB Redshift BigQuery Salesforce Hubspot Zendesk Spark The Log: What every software engineer should know about real-time data’s unifying abstraction by Jay Kreps RDBMS (Relational Database Management System) SaaS (Software as a Service) Change Data Capture Kafka Storm Google Cloud PubSub Amazon Kinesis Alooma Code Engine Zookeeper Idempotence Kafka Streams Kubernetes SOC2 Jython Docker Python Javascript Ruby Scala PII (Personally Identifiable Information) GDPR (General Data Protection Regulation) Amazon EMR (Elastic Map Reduce) Sequoia Capital Lightspeed Investors Redis Aerospike Cassandra MongoDB

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

Somebody wants to overthink their analytics tools? Tell 'em their dreamin'! We wanted to talk about open source and event analytics and Snowplow sits right at that intersection. Our guest Simon Rumble is the co-founder of Snowflake Analytics and one of the longest users of Snowplow. We wrap up the show with all the places you can find Simon and Tim in the next few months. Fun fact: You will also learn in this episode that conversion funnels go down the opposite direction in Australia. 

Over the last decade, Big Data was everywhere. Let's set the record straight on what is and isn't Big Data. We have been consumed by a conversation about data volumes when we should focus more on the immediate task at hand: Simplifying our work.

Some of us may have Big Data, but our quest to derive insights from it is measured in small slices of work that fit on your laptop or in your hand. Easy data is here— let's make the most of it.

📓 Resources Big Data is Dead: https://motherduck.com/blog/big-data-is-dead/ Small Data Manifesto: https://motherduck.com/blog/small-data-manifesto/ Small Data SF: https://www.smalldatasf.com/

➡️ Follow Us LinkedIn: https://linkedin.com/company/motherduck X/Twitter : https://twitter.com/motherduck Blog: https://motherduck.com/blog/

Explore the "Small Data" movement, a counter-narrative to the prevailing big data conference hype. This talk challenges the assumption that data scale is the most important feature of every workload, defining big data as any dataset too large for a single machine. We'll unpack why this distinction is crucial for modern data engineering and analytics, setting the stage for a new perspective on data architecture.

Delve into the history of big data systems, starting with the non-linear hardware costs that plagued early data practitioners. Discover how Google's foundational papers on GFS, MapReduce, and Bigtable led to the creation of Hadoop, fundamentally changing how we scale data processing. We'll break down the "big data tax"—the inherent latency and system complexity overhead required for distributed systems to function, a critical concept for anyone evaluating data platforms.

Learn about the architectural cornerstone of the modern cloud data warehouse: the separation of storage and compute. This design, popularized by systems like Snowflake and Google BigQuery, allows storage to scale almost infinitely while compute resources are provisioned on-demand. Understand how this model paved the way for massive data lakes but also introduced new complexities and cost considerations that are often overlooked.

We examine the cracks appearing in the big data paradigm, especially for OLAP workloads. While systems like Snowflake are still dominant, the rise of powerful alternatives like DuckDB signals a shift. We reveal the hidden costs of big data analytics, exemplified by a petabyte-scale query costing nearly $6,000, and argue that for most use cases, it's too expensive to run computations over massive datasets.

The key to efficient data processing isn't your total data size, but the size of your "hot data" or working set. This talk argues that the revenge of the single node is here, as modern hardware can often handle the actual data queried without the overhead of the big data tax. This is a crucial optimization technique for reducing cost and improving performance in any data warehouse.

Discover the core principles for designing systems in a post-big data world. We'll show that since only 1 in 500 users run true big data queries, prioritizing simplicity over premature scaling is key. For low latency, process data close to the user with tools like DuckDB and SQLite. This local-first approach offers a compelling alternative to cloud-centric models, enabling faster, more cost-effective, and innovative data architectures.

Taking a first look at running dbt projects natively in Snowflake Workspaces, the advantages and disadvantages compared to dbt Cloud, and whether moving right now fits your usecase. With hands-on demonstration and some extra tips on connecting Snowflake to your remote git dbt repository.