An introduction to QuerySurge webinar

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.

Data warehousing is a critical component for analysing and extracting actionable insights from your data. Amazon Redshift allows you to deploy a scalable data warehouse in a matter of minutes and starts to analyse your data right away using your existing business intelligence tools.

Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.

The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.

Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

This document discusses the evolution of data pipelines at Databricks over time from 2014 to present day. Early pipelines involved copying data from S3 hourly, which did not scale. Later pipelines used Amazon Kinesis but led to performance issues with many small files. The document then introduces structured streaming and Delta Lake as better solutions. Structured streaming provides correctness while Delta Lake improves performance, scalability, and makes data management and GDPR compliance easier through features like ACID transactions, automatic schema management, and built-in deletion/update support.

Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.

Presto is an open source distributed SQL query engine that allows querying of data across different data sources. It was originally developed by Facebook and is now used by many companies. Presto uses connectors to query various data sources like HDFS, S3, Cassandra, MySQL, etc. through a single SQL interface. Companies like Facebook and Teradata use Presto in production environments to query large datasets across different data platforms.

In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.