Amazon Redshift For Data Analysts

U-SQL is the query language for big data analytics on the Azure Data Lake platform. This session will explore the unification of SQL and C# in this new query language, examples of combining data from external sources such as Azure SQL Database and Blob storage with Azure Data Lake store, creating and referencing assemblies, job submission and tools. The ADL platform will also be compared and contrasted to the HDInsight/Hadoop platform.

Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and use work load management. Learning Objectives: • Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities • Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently • Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features Who Should Attend: • Data Warehouse Developers, Big Data Architects, BI Managers, and Data Engineers

The document provides an overview of U-SQL, highlighting some differences from traditional SQL like C# keywords overlapping with SQL keywords, the ability to write C# expressions for data transformations, and supporting windowing functions, joins, and analytics capabilities. It also briefly covers topics like sorting, constant rowsets, inserts, and additional resources for learning more about U-SQL.

Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all of your data for a fraction of the cost of traditional data warehouses. In this webinar, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. Learning Objectives: • Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities • Learn how to design schemas and load data efficiently • Learn best practices for workload management, distribution and sort keys, and optimizing queries

Data Lakes have become a new tool in building modern data warehouse architectures. In this presentation we will introduce Microsoft's Azure Data Lake offering and its new big data processing language called U-SQL that makes Big Data Processing easy by combining the declarativity of SQL with the extensibility of C#. We will give you an initial introduction to U-SQL by explaining why we introduced U-SQL and showing with an example of how to analyze some tweet data with U-SQL and its extensibility capabilities and take you on an introductory tour of U-SQL that is geared towards existing SQL users. slides for SQL Saturday 635, Vancouver BC, Aug 2017

This document discusses data partitioning and distribution in U-SQL. It explains how to use partitioned tables to get benefits like partition elimination in queries. Finely partitioning tables on keys like date and hashing on other keys can improve query performance by pruning partitions and distributions. The document also covers data skew that can occur if one partition receives too much data, and provides options to address it like repartitioning the data or using multiple partitioning keys.

The document discusses GoPro's transition to a new data platform architecture. The old architecture had several clusters for different workloads which caused operational overhead and lack of elasticity. The new architecture separates storage and computing, uses S3 for storage and ephemeral instances as compute clusters. It also introduces a centralized Hive metastore and uses dynamic DDL to flexibly ingest and aggregate both batch and streaming data while allowing the schema to change on the fly. This improves cost, scalability and enables more advanced analytics capabilities.

You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms.

Hive was introduced to allow users to run SQL-like queries on large datasets stored in Hadoop. It provides a data warehouse solution built on Hadoop that allows easy data summarization, querying, and analysis of big data stored in HDFS. Hive uses HDFS for storage but stores metadata about databases and tables in MySQL or Derby databases. It allows users to run queries using HiveQL, which is similar to SQL, without needing to write complex MapReduce programs.

Interested in learning Hadoop, but you’re overwhelmed by the number of components in the Hadoop ecosystem? You’d like to get some hands on experience with Hadoop but you don’t know Linux or Java? This session will focus on giving a high level explanation of Hive and HiveQL and how you can use them to get started with Hadoop without knowing Linux or Java.

Amazon DynamoDB is a fully managed NoSQL database service provided by AWS that allows users to store and retrieve any amount of data in database tables. It automatically manages data traffic and maintains performance over multiple servers. DynamoDB is scalable, fast, durable, highly available, flexible, and cost-effective for customers. It relieves customers from the burden of operating and scaling their own distributed databases.

Stored procedure tuning and optimization t sql Basic and advanced techniques to prevent timeouts, delay, deadlocks

Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use workload management, tune your queries, and use Amazon Redshift's interleaved sorting features.

How to extract data from Amazon Redshift by FlyData, the leaders in the MySQL to Redshift real-time data replication.

This document provides examples and explanations of key concepts in Hive Query Language (HQL) including how to create and populate tables, load data into Hive, write queries, and descriptions of managed vs external tables, partitions, and buckets. It also summarizes Hive architecture, clients, metastore configurations, and HiveQL capabilities compared to SQL standards.

Getting started in DynamoDB ? basics in NoSQL? setting up and environment ? Code snippet? Local desktop installation?

Our blog post: http://www.flydata.com/blog/posts/scalability-of-amazon-redshift-data-loading-and-query-speeds

Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze your data for a fraction of the cost of traditional data warehouses. In this webinar, you will learn how to easily migrate your data from other data warehouses into Amazon Redshift, efficiently load your data with Amazon Redshift's massively parallel processing (MPP) capabilities, and automate data loading with AWS Lambda and AWS Data Pipeline. You will also learn about ETL tools from our partners to extract, transform, and prepare data from disparate data sources before loading it into Amazon Redshift. Learning Objectives: Understand common patterns for migrating your data to Amazon Redshift See live examples of the Copy command that fully parallelizes data ingestion Learn how to automate the load process using AWS Lambda & AWS Data Pipleline Techniques for real time data loading Options for ETL tools from our partners