Building Lakehouses on Delta Lake with SQL Analytics Primer

Doug Bateman, a principal data engineering instructor at Databricks, presented on how to build a Lakehouse architecture. He began by introducing himself and his background. He then discussed the goals of describing key Lakehouse features, explaining how Delta Lake enables it, and developing a sample Lakehouse using Databricks. The key aspects of a Lakehouse are that it supports diverse data types and workloads while enabling using BI tools directly on source data. Delta Lake provides reliability, consistency, and performance through its ACID transactions, automatic file consolidation, and integration with Spark. Bateman concluded with a demo of creating a Lakehouse.

Enterprise data architectures usually contain many systems��data lakes, message queues, and data warehouses—that data must pass through before it can be analyzed. Each transfer step between systems adds a delay and a potential source of errors. What if we could remove all these steps? In recent years, cloud storage and new open source systems have enabled a radically new architecture: the lakehouse, an ACID transactional layer over cloud storage that can provide streaming, management features, indexing, and high-performance access similar to a data warehouse. Thousands of organizations including the largest Internet companies are now using lakehouses to replace separate data lake, warehouse and streaming systems and deliver high-quality data faster internally. I’ll discuss the key trends and recent advances in this area based on Delta Lake, the most widely used open source lakehouse platform, which was developed at Databricks.

So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.

The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.

Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.

In this talk we will present how Databricks has enabled the author to achieve more with data, enabling one person to build a coherent data project with data engineering, analysis and science components, with better collaboration, better productionalization methods, with larger datasets and faster. The talk will include a demo that will illustrate how the multiple functionalities of Databricks help to build a coherent data project with Databricks jobs, Delta Lake and auto-loader for data engineering, SQL Analytics for Data Analysis, Spark ML and MLFlow for data science, and Projects for collaboration.

Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2OUz6dt. Chris Riccomini talks about the current state-of-the-art in data pipelines and data warehousing, and shares some of the solutions to current problems dealing with data streaming and warehousing. Filmed at qconsf.com. Chris Riccomini works as a Software Engineer at WePay.

Delta Lake is an open source storage layer that sits on top of data lakes and brings ACID transactions and reliability to Apache Spark. It addresses challenges with data lakes like lack of schema enforcement and transactions. Delta Lake provides features like ACID transactions, scalable metadata handling, schema enforcement and evolution, time travel/data versioning, and unified batch and streaming processing. Delta Lake stores data in Apache Parquet format and uses a transaction log to track changes and ensure consistency even for large datasets. It allows for updates, deletes, and merges while enforcing schemas during writes.

The document discusses modernizing a healthcare organization's data platform from version 1.0 to 2.0 using Azure Databricks. Version 1.0 used Azure HDInsight (HDI) which was challenging to scale and maintain. It presented performance issues and lacked integrations. Version 2.0 with Databricks will provide improved scalability, cost optimization, governance, and ease of use through features like Delta Lake, Unity Catalog, and collaborative notebooks. This will help address challenges faced by consumers, data engineers, and the client.

Slides for the talk at AI in Production meetup: https://www.meetup.com/LearnDataScience/events/255723555/ Abstract: Demystifying Data Engineering With recent progress in the fields of big data analytics and machine learning, Data Engineering is an emerging discipline which is not well-defined and often poorly understood. In this talk, we aim to explain Data Engineering, its role in Data Science, the difference between a Data Scientist and a Data Engineer, the role of a Data Engineer and common concepts as well as commonly misunderstood ones found in Data Engineering. Toward the end of the talk, we will examine a typical Data Analytics system architecture.

The document provides an overview of the Databricks platform, which offers a unified environment for data engineering, analytics, and AI. It describes how Databricks addresses the complexity of managing data across siloed systems by providing a single "data lakehouse" platform where all data and analytics workloads can be run. Key features highlighted include Delta Lake for ACID transactions on data lakes, auto loader for streaming data ingestion, notebooks for interactive coding, and governance tools to securely share and catalog data and models.

The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include: - Defining top-down and bottom-up approaches to data management - Explaining what a data lake is and how Hadoop can function as the data lake - Describing how a modern data warehouse combines features of a traditional data warehouse and data lake - Discussing how federated querying allows data to be accessed across multiple sources - Highlighting benefits of implementing big data solutions in the cloud - Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (

The Microsoft Analytics Platform System (APS) is a turnkey appliance that provides a modern data warehouse with the ability to handle both relational and non-relational data. It uses a massively parallel processing (MPP) architecture with multiple CPUs running queries in parallel. The APS includes an integrated Hadoop distribution called HDInsight that allows users to query Hadoop data using T-SQL with PolyBase. This provides a single query interface and allows users to leverage existing SQL skills. The APS appliance is pre-configured with software and hardware optimized to deliver high performance at scale for data warehousing workloads.

Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.

This document discusses designing a modern data warehouse in Azure. It provides an overview of traditional vs. self-service data warehouses and their limitations. It also outlines challenges with current data warehouses around timeliness, flexibility, quality and findability. The document then discusses why organizations need a modern data warehouse based on criteria like customer experience, quality assurance and operational efficiency. It covers various approaches to ingesting, storing, preparing, modeling and serving data on Azure. Finally, it discusses architectures like the lambda architecture and common data models.

Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist. From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives. As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms. In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly. In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data). Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021. You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.

Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization. Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support. In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.

This document discusses ETL patterns in the cloud using Azure Data Factory. It covers topics like ETL vs ELT, scaling ETL in the cloud, handling flexible schemas, and using ADF for orchestration. Key points include staging data in low-cost storage before processing, using ADF's integration runtime to process data both on-premises and in the cloud, and building resilient data flows that can handle schema drift.

The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.

Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years. Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines. In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.

This document discusses techniques for optimizing Power BI performance. It recommends tracing queries using DAX Studio to identify slow queries and refresh times. Tracing tools like SQL Profiler and log files can provide insights into issues occurring in the data sources, Power BI layer, and across the network. Focusing on optimization by addressing wait times through a scientific process can help resolve long-term performance problems.

Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both. Topics include: - Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline - How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution - How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency - How Snowflake + Spark enables production and ad hoc analytics on a single repository of data

This document discusses different data types and data models. It begins by describing unstructured, semi-structured, and structured data. It then discusses relational and non-relational data models. The document notes that big data can include any of these data types and models. It provides an overview of Microsoft's data management and analytics platform and tools for working with structured, semi-structured, and unstructured data at varying scales. These include offerings like SQL Server, Azure SQL Database, Azure Data Lake Store, Azure Data Lake Analytics, HDInsight and Azure Data Warehouse.

This document discusses ETL patterns in the cloud using Azure Data Factory. It covers topics like ETL vs ELT, the importance of scale and flexible schemas in cloud ETL, and how Azure Data Factory supports workflows, templates, and integration with on-premises and cloud data. It also provides examples of nightly ETL data flows, handling schema drift, loading dimensional models, and data science scenarios using Azure data services.