Transform complex data engineering with just a spoken request. See how Maia, Matillion's agentic AI solution, autonomously designs, builds, and monitors data pipelines in minutes instead of hours—addressing the data engineering talent gap while freeing technical teams for strategic work.

This session features Richard Lewis, Senior Solution Architect at Matillion, in conversation with Daniel Adams, Global Analytics Manager, from Edmund Optics, as they share how data integration, AI, and cloud technologies transformed technical support and accelerated time to value. Edmund Optics’ team of 50 highly skilled engineers faced a flood of repetitive technical inquiries, averaging 25 minutes per response, across a catalog of 34,000+ components. To streamline support, they partnered with Matillion, Snap Analytics, and Snowflake to build an AI-powered chatbot. Deployed in just 10 weeks, the chatbot delivers instant, consistent answers to common queries, freeing engineers to tackle complex issues and dramatically improving support efficiency.

Real-time databases unlock the full potential of AI applications (and Agents). Randy draws on his experience at SingleStore and Matillion to show why performance and strong data foundations are essential for building AI.

This session will explore the evolving role of data engineers. Data engineering is currently a bottleneck due to overwhelming requests and complex knowledge work. Maia acts as a "digital data engineer" or a "virtual data team" that amplifies productivity by 100x. It enables users, from skilled engineers to citizen data analysts, to author pipelines in natural business language. The session will demonstrate Maia's ability to accelerate mundane and advanced tasks,troubleshoot and debug pipelines in real-time, and generate high-quality, auditable pipelines using Matillion's proprietary, human-readable Data Pipeline Language (DPL), which avoids "spaghetti code" common with generic LLMs.

This session features Joe, Matillion's Principal Solution Architect, in conversation with Alex R from EDF, a Lighthouse customer. EDF has already experienced the business value of Maia, with a 75% reduction in data science product delivery time and streamlining hundreds of jobs into a unified system. The discussion will highlight how Maia delivers

unprecedented productivity gains by helping customers move from legacy systems to an AI-driven data future, addressing tech consolidation and eliminating data friction. The Lighthouse program grants participants direct access to Matillion's product and engineering teams, fostering joint development and shared roadmaps, and aims to produce public-facing case studies

This book is your guide to the modern market of data analytics platforms and the benefits of using Snowflake, the data warehouse built for the cloud. As organizations increasingly rely on modern cloud data platforms, the core of any analytics framework—the data warehouse—is more important than ever. This updated 2nd edition ensures you are ready to make the most of the industry’s leading data warehouse. This book will onboard you to Snowflake and present best practices for deploying and using the Snowflake data warehouse. The book also covers modern analytics architecture, integration with leading analytics software such as Matillion ETL, Tableau, and Databricks, and migration scenarios for on-premises legacy data warehouses. This new edition includes expanded coverage of SnowPark for developing complex data applications, an introduction to managing large datasets with Apache Iceberg tables, and instructions for creating interactive data applications using Streamlit, ensuring readers are equipped with the latest advancements in Snowflake's capabilities. What You Will Learn Master key functionalities of Snowflake Set up security and access with cluster Bulk load data into Snowflake using the COPY command Migrate from a legacy data warehouse to Snowflake Integrate the Snowflake data platform with modern business intelligence (BI) and data integration tools Manage large datasets with Apache Iceberg Tables Implement continuous data loading with Snowpipe and Dynamic Tables Who This Book Is For Data professionals, business analysts, IT administrators, and existing or potential Snowflake users

Bayada is transforming its data ecosystem by consolidating Matillion+Snowflake and SSIS+SQL Server into a unified Enterprise Data Platform powered by Databricks. Using Databricks' Medallion architecture, this platform enables seamless data integration, advanced analytics and machine learning across critical domains like general ledger, recruitment and activity-based costing. Databricks was selected for its scalability, real-time analytics and ability to handle both structured and unstructured data, positioning Bayada for future growth. The migration aims to reduce data processing times by 35%, improve reporting accuracy and cut reconciliation efforts by 40%. Operational costs are projected to decrease by 20%, while real-time analytics is expected to boost efficiency by 15%. Join this session to learn how Bayada is leveraging Databricks to build a high-performance data platform that accelerates insights, drives efficiency and fosters innovation organization-wide.

Matthew Scullion (CEO, Co-Founder of Matillion) joins me to chat about the future of data engineering, namely agentic data engineering teams.

What does this new world look like? Matthew shares some ideas of what he's building at Matillion, and the broader context of what agentic AI means for the data ecosystem, teams, and workflows.

In today’s data-driven world, organizations are increasingly challenged to extract meaningful insights from both structured and unstructured data. Join Matillion and Snowflake in this exclusive session where we explore the transformative power of generative AI within data pipelines, leveraging Snowflake's robust platform and Cortex's advanced capabilities.

This session will guide you through practical use cases that demonstrate how to integrate unstructured and structured data to unlock new insights seamlessly:

Transforming Unstructured Data into Actionable Intelligence:

Discover how to convert customer reviews into structured data by labeling actionable items, extracting product names, and assessing sentiment. We’ll walk you through creating a BI dashboard and generating reports that highlight actionable defects and desired features, empowering your team to make data-driven decisions.

Generating Executive Summaries from Structured Data:

Learn how to generate insightful commentary from your existing structured data. We’ll show you how to create concise executive summaries and trend analyses, integrating contextual information such as competitor activities or regional updates. Imagine automating a weekly Slack message to your leadership team, complete with key sales trends and strategic insights.

Throughout this session, we will showcase the capabilities of Cortex, demonstrating how it can be seamlessly integrated into your data pipelines to enhance your data processing workflows. Whether you’re looking to improve customer experience, drive operational efficiency, or stay ahead of the competition, this session will provide you with the tools and techniques to harness the full potential of your data.

Join us to learn how to turn data into actionable insights that drive business success.

Premier Foods wanted to modernize its approach to data, building an architecture that would provide actionable insights, unlock vital data from business systems to simplify processes, and reduce risk.   

Join this session to learn now Premier Foods, working with Snap Analytics, implemented Matillion to deliver 15 different data solutions across the organization, including plastics reporting, customer service, manufacturing, sales reporting, procurement and product development.

Find out how they saved over 500 days of manual work and hundreds of thousands of pounds through decommissioning unnecessary data activities.

I recap two events from this week - Matillion's Data Unlocked and the first MLOps Community event in Silicon Valley, hosted at Coactive.ai's office. Lots to unpack in 8 minutes, so let's get going.

Summary

Cloud data warehouses and the introduction of the ELT paradigm has led to the creation of multiple options for flexible data integration, with a roughly equal distribution of commercial and open source options. The challenge is that most of those options are complex to operate and exist in their own silo. The dlt project was created to eliminate overhead and bring data integration into your full control as a library component of your overall data system. In this episode Adrian Brudaru explains how it works, the benefits that it provides over other data integration solutions, and how you can start building pipelines today.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Your host is Tobias Macey and today I'm interviewing Adrian Brudaru about dlt, an open source python library for data loading

Interview

Introduction How did you get involved in the area of data management? Can you describe what dlt is and the story behind it?

What is the problem you want to solve with dlt? Who is the target audience?

The obvious comparison is with systems like Singer/Meltano/Airbyte in the open source space, or Fivetran/Matillion/etc. in the commercial space. What are the complexities or limitations of those tools that leave an opening for dlt? Can you describe how dlt is implemented? What are the benefits of building it in Python? How have the design and goals of the project changed since you first started working on it? How does that language choice influence the performance and scaling characteristics? What problems do users solve with dlt? What are the interfaces available for extending/customizing/integrating with dlt? Can you talk through the process of adding a new source/destination? What is the workflow for someone building a pipeline with dlt? How does the experience scale when supporting multiple connections? Given the limited scope of extract and load, and the composable design of dlt it seems like a purpose built companion to dbt (down to th

OurFamilyWizard helps families living separately thrive, empowering parents with needed tools after divorce or separation. Migrating to a modern data stack built on a Databricks Delta Lake seemed like the obvious choice for OurFamilyWizard to start integrating 20 years of on-prem Oracle data with event tracking and SaaS cloud data, but they needed tools to do it. OurFamilyWizard turned to Matillion, a powerful and intuitive solution, to quickly load, combine, and transform source data into reporting tables and data marts, and empower them to turn raw data into information the organization can use to make decisions.

In this session, Beth Mattson, OurFamilyWizard Senior Data Engineer, will detail how Matillion helped OurFamilyWizard migrate their data to Databricks fast and provided end-to-end ETL capabilities. In addition, Jamie Baker, Matillion Director of Product Management, will give a brief demo and discuss the Matillion and Databricks partnership and what is on the horizon.

Talk by: Jamie Baker and Beth Mattson

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

In this presentation, Matillion’s Sarah Pollitt, Group Product Manager for ETL, will discuss how you can use Matillion to load data from popular data sources such as Salesforce, SAP, and over a hundred out-of-the-box connectors into your data lakehouse. You can quickly transform this data using powerful tools like Matillion or dbt, or your own custom notebooks, to derive valuable insights. She will also explore how you can run streaming pipelines to ensure real-time data processing, and how you can extract and manage this data using popular governance tools such as Alation or Collibra, ensuring compliance and data quality. Finally, Sarah will showcase how you can seamlessly integrate this data into your analytics tools of choice, such as Thoughtspot, PowerBI, or any other analytics tool that fits your organization's needs.

Talk by: Rick Wear

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Change data capture (CDC) is an increasingly common technology used in real-time machine learning and AI data pipelines. When paired with Databricks Delta Lake, it provides organizations with a number of benefits including lower data processing costs and highly responsive analytics applications. This session will provide a detailed overview of Matillion’s new CDC capabilities and how the integration of these capabilities with Delta Lake on Databricks can help you manage dataset changes, making it easy to automate the capture, transformation, and enrichment of data in near real time. Attend this session and see the advantages of a Matillion’s CDC capabilities to simplify real time data capture and analytics in your Delta Lake.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/

Summary The interfaces and design cues that a tool offers can have a massive impact on who is able to use it and the tasks that they are able to perform. With an eye to making data workflows more accessible to everyone in an organization Raj Bains and his team at Prophecy designed a powerful and extensible low-code platform that lets technical and non-technical users scale data flows without forcing everyone into the same layers of abstraction. In this episode he explores the tension between code-first and no-code utilities and how he is working to balance the strengths without falling prey to their shortcomings.

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! 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 So now your modern data stack is set up. How is everyone going to find the data they need, and understand it? Select Star is a data discovery platform that automatically analyzes & documents your data. For every table in Select Star, you can find out where the data originated, which dashboards are built on top of it, who’s using it in the company, and how they’re using it, all the way down to the SQL queries. Best of all, it’s simple to set up, and easy for both engineering and operations teams to use. With Select Star’s data catalog, a single source of truth for your data is built in minutes, even across thousands of datasets. Try it out for free and double the length of your free trial today at dataengineeringpodcast.com/selectstar. You’ll also get a swag package when you continue on a paid plan. Your host is Tobias Macey and today I’m interviewing Raj Bains about how improving the user experience for data tools can make your work as a data engineer better and easier

Interview

Introduction How did you get involved in the area of data management? What are the broad categories of data tool designs that are available currently and how does that impact what is possible with them?

What are the points of friction that are introduced by the tools? Can you share some of the types of workarounds or wasted effort that are made necessary by those design elements?

What are the core design principles that you have built into Prophecy to address these shortcomings?

How do those user experience changes improve the quality and speed of work for data engineers?

How has the Prophecy platform changed since we last spoke almost a year ago? What are the tradeoffs of low code systems for productivity vs. flexibility and creativity? What are the most interesting, innovative, or unexpected approaches to developer experience that you have seen for data tools? What are the most interesting, unexpected, or challenging lessons that you have learned while working on user experience optimization for data tooling at Prophecy? When is it more important to optimize for computational efficiency over developer productivity? What do you have planned for the future of Prophecy?

Contact Info

LinkedIn @_raj_bains 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

Links

Prophecy

Podcast Episode

CUDA Clustrix Hortonworks Apache Hive Compilerworks

Podcast Episode

Airflow Databricks Fivetran

Podcast Episode

Airbyte

Podcast Episode

Streamsets Change Data Capture Apache Pig Spark Scala Ab Initio Type 2 Slowly Changing Dimensions AWS Deequ Matillion

Podcast Episode

Prophecy SaaS

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

Support Data Engineering Podcast

Summary The predominant pattern for data integration in the cloud has become extract, load, and then transform or ELT. Matillion was an early innovator of that approach and in this episode CTO Ed Thompson explains how they have evolved the platform to keep pace with the rapidly changing ecosystem. He describes how the platform is architected, the challenges related to selling cloud technologies into enterprise organizations, and how you can adopt Matillion for your own workflows to reduce the maintenance burden of data integration workflows.

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! 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 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 or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit http://www.dataengineeringpodcast.com/montecarlo?utm_source=rss&utm_medium=rss to learn more. Your host is Tobias Macey and today I’m interviewing Ed Thompson about Matillion, a cloud-native data integration platform for accelerating your time to analytics

Interview

Introduction How did you get involved in the area of data management?

Summary Data integration is a critical piece of every data pipeline, yet it is still far from being a solved problem. There are a number of managed platforms available, but the list of options for an open source system that supports a large variety of sources and destinations is still embarrasingly short. The team at Airbyte is adding a new entry to that list with the goal of making robust and easy to use data integration more accessible to teams who want or need to maintain full control of their data. In this episode co-founders John Lafleur and Michel Tricot share the story of how and why they created Airbyte, discuss the project’s design and architecture, and explain their vision of what an open soure data integration platform should offer. If you are struggling to maintain your extract and load pipelines or spending time on integrating with a new system when you would prefer to be working on other projects then this is definitely a conversation worth listening to.

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 Michel Tricot and John Lafleur about Airbyte, an open source framework for building data integration pipelines.

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Airbyte is and the story behind it? Businesses and data engineers have a variety of options for how to manage their data integration. How would you characterize the overall landscape and how does Airbyte distinguish itself in that space? How would you characterize your target users?

How have those personas instructed the priorities and design of Airbyte? What do you see as the benefits and tradeoffs of a UI oriented data integration platform as compared to a code first approach?

what are the complex/challenging elements of data integration that makes it such a slippery problem? motivation for creating open source ELT as a business Can you describe how the Airbyte platform is implemented?

What was your motivation for choosing Java as the primary language?

incidental complexity of forcing all connectors to be packaged as containers shortcomings of the Singer specification/motivation for creating a backwards incompatible interface perceived potential for community adoption of Airbyte specification tradeoffs of using JSON as interchange format vs. e.g. protobuf/gRPC/Avro/etc.

information lost when converting records to JSON types/how to preserve that information (e.g. field constraints, valid enums, etc.)

interfaces/extension points for integrating with other tools, e.g. Dagster abstraction layers for simplifying implementation of new connectors tradeoffs of storing all connectors in a monorepo with the Airbyte core

impact of community adoption/contributions

What is involved in setting up an Airbyte installation? What are the available axes for scaling an Airbyte deployment? challenges of setting up and maintaining CI environment for Airbyte How are you managing governance and long term sustainability of the project? What are some of the most interesting, unexpected, or innovative ways that you have seen Airbyte used? What are the most interesting, unexpected, or challenging lessons that you have learned while building Airbyte? When is Airbyte the wrong choice? What do you have planned for the future of the project?

Contact Info

Michel

LinkedIn @MichelTricot on Twitter michel-tricot on GitHub

John

LinkedIn @JeanLafleur on Twitter johnlafleur on GitHub

Parting Question

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

Links

Airbyte Liveramp Fivetran

Podcast Episode

Stitch Data Matillion DataCoral

Podcast Episode

Singer Meltano

Podcast Episode

Airflow

Podcast.init Episode

Kotlin Docker Monorepo Airbyte Specification Great Expectations

Podcast Episode

Dagster

Data Engineering Podcast Episode Podcast.init Episode

Prefect

Podcast Episode

DBT

Podcast Episode

Kubernetes Snowflake

Podcast Episode

Redshift Presto Spark Parquet

Podcast Episode

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

Support Data Engineering Podcast

Explore the modern market of data analytics platforms and the benefits of using Snowflake computing, the data warehouse built for the cloud. With the rise of cloud technologies, organizations prefer to deploy their analytics using cloud providers such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. Cloud vendors are offering modern data platforms for building cloud analytics solutions to collect data and consolidate into single storage solutions that provide insights for business users. The core of any analytics framework is the data warehouse, and previously customers did not have many choices of platform to use. Snowflake was built specifically for the cloud and it is a true game changer for the analytics market. This book will help onboard you to Snowflake, present best practices to deploy, and use the Snowflake data warehouse. In addition, it covers modern analytics architecture and use cases. It provides use cases of integration with leading analytics software such as Matillion ETL, Tableau, and Databricks. Finally, it covers migration scenarios for on-premise legacy data warehouses. What You Will Learn Know the key functionalities of Snowflake Set up security and access with cluster Bulk load data into Snowflake using the COPY command Migrate from a legacy data warehouse to Snowflake integrate the Snowflake data platform with modern business intelligence (BI) and data integration tools Who This Book Is For Those working with data warehouse and business intelligence (BI) technologies, and existing and potential Snowflake users