Summary

Software development involves an interesting balance of creativity and repetition of patterns. Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. In this episode Eran Yahav shares the journey that he has taken in building this product and the ways that it enhances the ability of humans to get their work done, and when the humans have to adapt to the tool.

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 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 Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. 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! Your host is Tobias Macey and today I'm interviewing Eran Yahav about building an AI powered developer assistant at Tabnine

Interview

Introduction How did you get involved in machine learning? Can you describe what Tabnine is and the story behind it? What are the individual and organizational motivations for using AI to generate code?

What are the real-world limitations of generative AI for creating software? (e.g. size/complexity of the outputs, naming conventions, etc.) What are the elements of skepticism/overs

Summary

Databases are the core of most applications, but they are often treated as inscrutable black boxes. When an application is slow, there is a good probability that the database needs some attention. In this episode Lukas Fittl shares some hard-won wisdom about the causes and solution of many performance bottlenecks and the work that he is doing to shine some light on PostgreSQL to make it easier to understand how to keep it running smoothly.

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! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. 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 Lukas Fittl about optimizing your database performance and tips for tuning Postgres

Interview

Introduction How did you get involved in the area of data management? What are the different ways that database performance problems impact the business? What are the most common contributors to performance issues? What are the useful signals that indicate performance challenges in the database?

For a given symptom, what are the steps that you recommend for determining the proximate cause?

What are the potential negative impacts to be aware of when tu

In this session, gain strategic guidance on how to deploy dbt Cloud seamlessly to a team of 5-85 people. You'll learn best practices across development and automation that will ensure stability and high standards as you scale the number of developers using dbt Cloud and the number of models built up to the low thousands.

This session is a great fit for folks with beginner through intermediate levels of experience with dbt. In basketball terms, this talk covers mid-range shooting skills, but does not go into detail about 3-pointers, let alone half court shots. Likewise, this talk is not for people who are brand new to dbt and aren't familiar with the basic architecture of dbt and the modern data stack.

Speakers: Chris Davis, Senior Staff Engineer, Udemy, Inc.

Register for Coalesce at https://coalesce.getdbt.com

With the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data's quality. Delta Lake's open source format offers a robust lakehouse framework over platforms like Amazon S3, ADLS, and GCS. This practical book shows data engineers, data scientists, and data analysts how to get Delta Lake and its features up and running. The ultimate goal of building data pipelines and applications is to gain insights from data. You'll understand how your storage solution choice determines the robustness and performance of the data pipeline, from raw data to insights. You'll learn how to: Use modern data management and data engineering techniques Understand how ACID transactions bring reliability to data lakes at scale Run streaming and batch jobs against your data lake concurrently Execute update, delete, and merge commands against your data lake Use time travel to roll back and examine previous data versions Build a streaming data quality pipeline following the medallion architecture

Rust is a unique language whose traits make it very appealing for data engineering. In this session, we'll walk through the different aspects of the language that make it such a good fit for big data processing including: how it improves performance and how it provides greater safety guarantees and compatibility with a wide range of existing tools that make it well positioned to become a major building block for the future of analytics.

We will also take a hands-on look through real code examples at a few emerging technologies built on top of Rust that utilize these capabilities, and learn how to apply them to our modern lakehouse architecture.

Talk by: Oz Katz

Here’s more to explore: Why the Data Lakehouse Is Your next Data Warehouse: https://dbricks.co/3Pt5unq Lakehouse Fundamentals Training: https://dbricks.co/44ancQs

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

Databricks workflows has come a long way since the initial days of orchestrating simple notebooks and jar/wheel files. Now we can orchestrate multi-task jobs and create a chain of tasks with lineage and DAG with either fan-in or fan-out among multiple other patterns or even run another Databricks job directly inside another job.

Databricks workflows takes its tag: “orchestrate anything anywhere” pretty seriously and is a truly fully-managed, cloud-native orchestrator to orchestrate diverse workloads like Delta Live Tables, SQL, Notebooks, Jars, Python Wheels, dbt, SQL, Apache Spark™, ML pipelines with excellent monitoring, alerting and observability capabilities as well. Basically, it is a one-stop product for all orchestration needs for an efficient lakehouse. And what is even better is, it gives full flexibility of running your jobs in a cloud-agnostic and cloud-independent way and is available across AWS, Azure and GCP.

In this session, we will discuss and deep dive on some of the very interesting features and will showcase end-to-end demos of the features which will allow you to take full advantage of Databricks workflows for orchestrating the lakehouse.

Talk by: Prashanth Babu

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

Across industries, a multicloud setup has quickly become the reality for large organizations. Multi-cloud introduces new governance challenges as permissions models often do not translate from one cloud to the other and if they do, are insufficiently granular to accommodate privacy requirements and principles of least privilege. This problem can be especially acute for data and AI workloads that rely on sharing and aggregating large and diverse data sources across business unit boundaries and where governance models need to incorporate assets such as table rows/columns and ML features and models.

In this session, we will provide guidelines on how best to overcome these challenges for companies that have adopted the Databricks Lakehouse as their collaborative space for data teams across the organization, by exploiting some of the unique product features of the Databricks platform. We will focus on a common scenario: a data platform team providing data assets to two different ML teams, one using the same cloud and the other one using a different cloud.

We will explain the step-by-step setup of a unified governance model by leveraging the following components and conventions:

• Unity Catalog for implementing fine-grained access control across all data assets: files in cloud storage, rows and columns in tables and ML features and models
• The Databricks Terraform provider to automatically enforce guardrails and permissions across clouds
• Account level SSO Integration and identity federation to centralize administer access across workspaces
• Delta sharing to seamlessly propagate changes in provider data sets to consumers in near real-time
• Centralized audit logging for a unified view on what asset was accessed by whom

Talk by: Ioannis Papadopoulos and Volker Tjaden

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

Instacart has gone through immense growth during the pandemic and the trend continues. Instacart ads is no exception in this growth story. We have launched many new product lines including display and video ads covering the full advertising funnel to address the increasing demand of our retail partners. We have built advanced models to auto-suggest optimal bidding to increase the ROI for our CPG partners. Advertisers’ trust is the utmost priority and thus the quest to build a top-class ads measurement platform.

Ads data processing requires complex data verifications to update ads serving stats. In ETL pipelines these were implemented through files containing thousands of lines of raw SQL which were hard to scale, test, and iterate upon. Our data engineers used to spend hours testing small changes due to a lack of local testing mechanisms. These pain points stress our need for better tools. After some research, we chose Apache Spark™ as our preferred tool to rebuild ETLs, and the Databricks platform made this move easier. In this session, We'll share our journey to move our pipelines to Spark and Delta Lake on Databricks. With Spark, Scala, and Delta we solved many problems which were slowing the team’s productivity. Some key areas that will be covered include:

• Modular and composable code
• Unit testing framework
• Incremental event processing with spark structured streaming
• Granular resource tuning for better performance and cost efficacy

Other than the domain business logic, the problems discussed here are quite common for performing data processing at scale. We hope that sharing our learnings will benefit others who are going through similar growth challenges or migrating to Lakehouse.

Talk by: Devlina Das and Arthur Li

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

Delta Lake is an open-source storage format that can be ideally used for storing large-scale datasets, which can be used for single-node and distributed training of deep learning models. Delta Lake storage format gives deep learning practitioners unique data management capabilities for working with their datasets. The challenge is that, as of now, it’s not possible to use Delta Lake to train PyTorch models directly.

PyTorch community has recently introduced a Torchdata library for efficient data loading. This library supports many formats out of the box, but not Delta Lake. This talk will demonstrate using the Delta Lake storage format for single-node and distributed PyTorch training using the torchdata framework and standalone delta-rs Delta Lake implementation.

Talk by: Michael Shtelma

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

Tune into DoorDash's journey to migrate from a flaky ETL system with 24-hour data delays, to standardizing a CDC streaming pattern across more than 150 databases to produce near real-time data in a scalable, configurable, and reliable manner.

During this journey, understand how we use Delta Lake to build a self-serve, read-optimized data lake with data latencies of 15, whilst reducing operational overhead. Furthermore, understand how certain tradeoffs like conceding to a non-real-time system allow for multiple optimizations but still permit for OLTP query use-cases, and the benefits it provides.

Talk by: Ivan Peng and Phani Nalluri

Here’s more to explore: Big Book of Data Engineering: 2nd Edition: https://dbricks.co/3XpPgNV The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI

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

Stepping into this brave new digital world we are certain that data will be a central product for many organizations. The way to convey their knowledge and their assets will be through data and analytics. Delta Sharing was the world's first open protocol for secure and scalable real-time data sharing. Through our customer conversations, there is a lot of anticipation of how Delta Sharing can be extended to non-tabular assets, such as machine learning experiments and models.

In this session, we will cover how we extended the Delta Sharing protocol to other sharing workflows, enabling sharing of ML models, arbitrary files and more. The development resulted in Arcuate, a Databricks Labs project with a data sharing flavor. The session will start with the high-level approach and how it can be extended to cover other similar use cases. It will then move to our implementation and how it integrates seamlessly with Databricks-managed Delta Sharing server and notebooks. We finally conclude with lessons learned, and our visions for a future of data sharing and beyond

Talk by: Vuong Nguyen and Milos Colic

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

Data with low latency is important for real-time incident analysis and metrics. Though we have up-to-date data in OLTP databases, they cannot support those scenarios. Data need to be replicated to a data warehouse to serve queries using GroupBy and Join across multiple tables from different systems. At Coinbase, we designed SOON (Spark cOntinuOus iNgestion) based on Kafka, Kafka Connect, and Apache Spark™ as an incremental table replication solution to replicate tables of any size from any database to Delta Lake in a timely manner. It also supports Kafka events ingestion naturally.

SOON incrementally ingests Kafka events as appends, updates, and deletes to an existing table on Delta Lake. The events are grouped into two categories: CDC (change data capture) events generated by Kafka Connect source connectors, and non-CDC events by the frontend or backend services. Both types can be appended or merged into the Delta Lake. Non-CDC events can be in any format, but CDC events must be in the standard SOON CDC schema. We implemented Kafka Connect SMTs to transform raw CDC events into this standardized format. SOON unifies all streaming ingestion scenarios such that users only need to learn one onboarding experience and the team only needs to maintain one framework.

We care about the ingestion performance. The biggest append-only table onboarded has ingress traffic at hundreds of thousands events per second; the biggest CDC-merge table onboarded has a snapshot size of a few TBs and CDC update traffic at hundreds of thousands events per second. A lot of innovative ideas are incorporated in SOON to improve its performance, such as min-max range merge optimization, KMeans merge optimization, no-update merge for deduplication, generated columns as partitions, etc.

Talk by: Chen Guo

Here’s more to explore: Big Book of Data Engineering: 2nd Edition: https://dbricks.co/3XpPgNV The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI

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

Accurate, reliable, and timely data is critical for CPG companies to stay ahead in highly competitive retailer relationships, and for a company like the Hershey Company, the commercial relationship with Walmart is one of the most important. The team at Hershey found themselves with a looming deadline for their legacy analytics services and targeted a migration to the brand new Walmart Luminate API. Working in partnership with Advancing Analytics, the Hershey Company leveraged a metadata-driven Lakehouse Architecture to rapidly onboard the new Luminate API, helping the category management teams to overhaul how they measure, predict, and plan their business operations.

In this session, we will discuss the impact Luminate has had on Hershey's business covering key areas such as sales, supply chain, and retail field execution, and the technical building blocks that can be used to rapidly provision business users with the data they need, when they need it. We will discuss how key technologies enable this rapid approach, with Databricks Autoloader ingesting and shaping our data, Delta Streaming processing the data through the lakehouse and Databricks SQL providing a responsive serving layer. The session will include commentary as well as cover the technical journey.

Talk by: Simon Whiteley and Jordan Donmoyer

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

This session focuses on one of the first Unity Catalog implementations for a large-scale enterprise. In this scenario, a cloud scale analytics platform with 7500 active users based on the lakehouse approach is used. In addition, there is potential for 1500 further users who are subject to special governance rules. They are consuming more than 600 TB of data stored in Delta Lake - continuously growing at more than 1TB per day. This might grow due to local country data. Therefore, the existing data platform must be extended to enable users to combine global and local data from their countries. A new data management was required, which reflects the strict information security rules at a need to know base. Core requirements are: read only from global data, write into local and share the results.

Due to a very pronounced information security awareness and a lack of the technological possibilities it was not possible to interdisciplinary analyze and exchange data so easy or at all so far. Therefore, a lot of business potential and gains could not be identified and realized.

With the new developments in the technology used and the basis of the lakehouse approach, thanks to Unity Catalog, we were able to develop a solution that could meet high requirements for security and process. And enables globally secured interdisciplinary data exchange and analysis at scale. This solution enables the democratization of the data. This results not only in the ability to gain better insights for business management, but also to generate entirely new business cases or products that require a higher degree of data integration and encourage the culture to change. We highlight technical challenges and solutions, present best practices and point out benefits of implementing Unity catalog for enterprises.

Talk by: Artem Meshcheryakov and Pascal van Bellen

Here’s more to explore: Data, Analytics, and AI Governance: https://dbricks.co/44gu3YU

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

When creating streaming workloads with Databricks, it can sometimes be difficult to capture and understand the current structure of your source data. For example, what happens if you are ingesting JSON events from a vendor, and the keys are very sparsely populated, or contain dynamic content? Ideally, data engineers want to "lock in" a target schema in order to minimize complexity and maximize performance for known access patterns. What do you do when your data sources just don't cooperate with that vision? The first step is to quantify how far your current source data is drifting from your established Delta table. But how?

This session will demonstrate a way to capture and visual drift across all your streaming tables. The next question is, "Now that I see all of the data I'm missing, how do I selectively promote some of these keys into DataFrame columns?" The second half of this session will demonstrate precisely how to do a schema migration with minimal job downtime.

Talk by: Alexander Vanadio

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

Honeywell manages the control of equipment for hundreds of thousands of buildings worldwide. Many of our outcomes relating to energy and comfort rely on knowing where people are in the building at any one time. This is so we can target health and comfort conditions more suitably to areas where are more densely populated. Many of these buildings have Cisco IT infrastructure in them. Using their WIFI points and the RSSI signal strength from people’s laptops and phones, Cisco can calculate the number of people in each area of the building. Cisco Spaces offer this data up as a real-time streaming source. Honeywell HBT has utilized this stream of data by writing delta live table pipelines to consume this data source.

Honeywell buildings can now receive this firehose data from hundreds of concurrent customers and provide this occupancy data as a service to our vertical offerings in commercial, health, real estate and education. We will discuss the benefits of using DLT to handle this sort of incoming stream data, and illustrate the pain points we had and the resolutions we undertook in successfully receiving the stream of Cisco data. We will illustrate how our DLT pipeline was designed, and how it scaled to deal with huge quantities of real-time streaming data.

Talk by: Paul Mracek and Chris Inkpen

Here’s more to explore: Big Book of Data Engineering: 2nd Edition: https://dbricks.co/3XpPgNV The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI

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

Akamai is a leading content delivery network (CDN) and cybersecurity company operating hundreds of thousands of servers in more than 135 countries worldwide. In this session, we will share our experiences and lessons learned from building and maintaining the Web Security Analytics (WSA) product, an interactive analytics platform powered by Databricks and Delta Lake that enables customers to efficiently analyze and take informed action on a high volume of streaming security events.

The WSA platform must be able to serve hundreds of queries per minute, scanning hundreds of terabytes of data from a six petabyte data lake, with most queries returning results within ten seconds; for both aggregation queries and needle in a haystack queries. This session will cover how to use Databricks SQL warehouses and job clusters cost-effectively, and how to improve query performance using tools and techniques such as Delta Lake, Databricks Photon, and partitioning. This talk will be valuable for anyone looking to build and operate a high-performance analytics platform.

Talk by: Tomer Patel and Itai Yaffe

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 recent years, many businesses have adopted real-time streaming applications to enable faster decision making, quicker predictions, and improved customer experiences. Few of these applications are driving critical business use cases like financial fraud detection, loan application processing, personalized offers, etc. These business critical applications need robust disaster recovery strategies to recover from the catastrophic events to reduce the lost uptime. However, most organizations find it hard to set up disaster recovery for streaming applications as it involves continuous data flow. Streaming state and temporal behavior of data brings add complexities to the DR strategy. A reliable disaster recovery strategy includes backup, failover and failback approaches for the streaming application. Unlike the batch applications, these steps include many moving elements and need a very sophisticated approach to ensure that the services are failing over the DR region and meet the set RTO and RPO requirements.

In this session, we will cover following topics with a FINSERV use case demo: - Backup strategy: backup of delta tables, message bus services and checkpoint including offsets - Failover strategy: failover strategy to disable services in the primary region and start the services in the secondary region with minimum data loss - Failback strategy: failback strategy to restart the services in the primary region once all the services are restored - Common challenges and best practices for backup

Talk by: Shasidhar Eranti and Sachin Balgonda Patil

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

As the backbone of Australia’s supply chain, the Australia Rail Track Corporation (ARTC) plays a vital role in the management and monitoring of goods transportation across 8,500km of its rail network throughout Australia. ARTC provides weighbridges along their track which read train weights as they pass at speeds of up to 60 kilometers an hour. This information is highly valuable and is required both by ARTC and their customers to provide accurate haulage weight details, analyze technical equipment, and help ensure wagons have been loaded correctly.

A total of 750 trains run across a network of 8500 km in a day and generate real-time data at approximately 50 sensor platforms. With the help of structured streaming and Delta Lake, ARTC was able to analyze and store:

• Precise train location
• Weight of the train in real-time
• Train crossing time to the second level
• Train speed, temperature, sound frequency, and friction
• Train schedule lookups

Once all the IoT data has been pulled together from an IoT event hub, it is processed in real-time using structured streaming and stored in Delta Lake. To understand the train GPS location, API calls are then made per minute per train from the Lakehouse. API calls are made in real-time to another scheduling system to lookup customer info. Once the processed/enriched data is stored in Delta Lake, an API layer was also created on top of it to expose this data to all consumers.

The outcome: increased transparency on weight data as it is now made available to customers; we built a digital data ecosystem that now ARTC’s customers use to meet their KPIs/ planning; the ability to determine temporary speed restrictions across the network to improve train scheduling accuracy and also schedule network maintenance based on train schedules and speed.

Talk by: Deepak Sekar and Harsh Mishra

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

As part of Comcast Effectv’s transformation into a completely digital advertising agency, it was key to develop an approach to manage and remediate data quality issues related to customer data so that the sales organization is using reliable data to enable data-driven decision making. Like many organizations, Effectv's customer lifecycle processes are spread across many systems utilizing various integrations between them. This results in key challenges like duplicate and redundant customer data that requires rationalization and remediation. Data is at the core of Effectv’s modernization journey with the intended result of winning more business, accelerating order fulfillment, reducing make-goods and identifying revenue.

In partnership with Slalom Consulting, Comcast Effectv built a traditional lakehouse on Databricks to ingest data from all of these systems but with a twist; they anchored every engineering decision in how it will enable their data governance program.

In this session, we will touch upon the data transformation journey at Effectv and dive deeper into the implementation of data governance leveraging Databricks solutions such as Delta Lake, Unity Catalog and DB SQL. Key focus areas include how we baked master data management into our pipelines by automating the matching and survivorship process, and bringing it all together for the data consumer via DBSQL to use our certified assets in bronze, silver and gold layers.

By making thoughtful decisions about structuring data in Unity Catalog and baking MDM into ETL pipelines, you can greatly increase the quality, reliability, and adoption of single-source-of-truth data so your business users can stop spending cycles on wrangling data and spend more time developing actionable insights for your business.

Talk by: Maggie Davis and Risha Ravindranath

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