Many organizations rely on complex cloud data architectures that create silos between applications, users and data. This fragmentation makes it difficult to access accurate, up-to-date information for analytics, often resulting in the use of outdated data. Enter the lakehouse, a modern data architecture that unifies data, AI, and analytics in a single location.

This session explores why the lakehouse is the best data warehouse, featuring success stories, use cases and best practices from industry experts. You'll discover how to unify and govern business-critical data at scale to build a curated data lake for data warehousing, SQL and BI. Additionally, you'll learn how Databricks SQL can help lower costs and get started in seconds with on-demand, elastic SQL serverless warehouses, and how to empower analytics engineers and analysts to quickly find and share new insights using their preferred BI and SQL tools such as Fivetran, dbt, Tableau, or Power BI.

Talk by: Miranda Luna and Cyrielle Simeone

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Vector databases such as ElasticSearch and Pinecone offer fast ingestion and querying on vector embeddings with ANNs. However, they typically do not decouple compute and storage, making them hard to integrate in production data stacks. Because data storage in these databases is expensive and not easily accessible, data teams typically maintain ETL pipelines to offload historical embedding data to blob stores. When that data needs to be queried, they get loaded back into the vector database in another ETL process. This is reminiscent of loading data from OLTP database to cloud storage, then loading said data into an OLAP warehouse for offline analytics.

Recently, “lakehouse” offerings allow direct OLAP querying on cloud storage, removing the need for the second ETL step. The same could be done for embedding data. While embedding storage in blob stores cannot satisfy the high TPS requirements in online settings, we argue it’s sufficient for offline analytics use cases like slicing and dicing data based on embedding clusters. Instead of loading the embedding data back into the vector database for offline analytics, we propose direct processing on embeddings stored in Parquet files in Delta Lake. You will see that offline embedding workloads typically touch a large portion of the stored embeddings without the need for random access.

As a result, the workload is entirely bound by network throughput instead of latency, making it quite suitable for blob storage backends. On a test one billion vector dataset, ETL into cloud storage takes around one hour on a dedicated GPU instance, while batched nearest neighbor search can be done in under one minute with four CPU instances. We believe future “lakehouses” will ship with native support for these embedding workloads.

Talk by: Tony Wang and Chang She

Here’s more to explore: State of Data + AI Report: https://dbricks.co/44i2HBp Databricks named a Leader in 2022 Gartner® Magic QuadrantTM CDBMS: https://dbricks.co/3phw20d

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Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable. Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden.

This post explores different architecture to build serverless Kafka and Spark multi-cloud architectures across regions and continents. We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data lakehouse. Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.

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In this talk, we will outline our data pipelines and demo dashboards developed on top of the resulting elasticsearch index. This tool enables queries for terms or phrases in the raw documents to be executed together with any associated EMR patient data filters within 1-2 second for a data set containing millions of records/documents. Finally, the dashboards are simple to use and enable Real World Evidence data stakeholders to gain real-time statistical insight into the comprehensive patient information available.

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One of the largest State based health exchanges in the country was looking to modernize their data warehouse (DWH) environment to support the vision that every decision to design, implement and evaluate their state-based health exchange portal is informed by timely and rigorous evidence about its consumers’ experiences. The scope of the project was to replace existing Oracle-based DWH with an analytics platform that could support a much broader range of requirements with an ability to provide unified analytics capabilities including machine learning. The modernized analytics platform comprises a cloud native data lake and DWH solution using Databricks. The solution provides significantly higher performance and elastic scalability to better handle larger and varying data volumes with a much lower cost of ownership compared to the existing solution. In this session, we will walk through the rationale behind tool selection, solution architecture, project timeline and benefits expected.

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Most organizations routinely operate their business with complex cloud data architectures that silo applications, users and data. As a result, there is no single source of truth of data for analytics, and most analysis is performed with stale data. To solve these challenges, the lakehouse has emerged as the new standard for data architecture, with the promise to unify data, AI and analytic workloads in one place. In this session, we will cover why the data lakehouse is the next best data warehouse. You will hear from the experts success stories, use cases, and best practices learned from the field and discover how the data lakehouse ingests, stores and governs business-critical data at scale to build a curated data lake for data warehousing, SQL and BI workloads. You will also learn how Databricks SQL can help you lower costs and get started in seconds with instant, elastic SQL serverless compute, and how to empower every analytics engineers and analysts to quickly find and share new insights using their favorite BI and SQL tools, like Fivetran, dbt, Tableau or PowerBI.

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/