Tag: data warehouse

Feed: AWS Big Data Blog. This is the sixth in a series of posts. We’re excited to share dozens of new features to automate your schema conversion; preserve your investment in existing scripts, reports, and applications; accelerate query performance; and potentially simplify your migrations from legacy data warehouses to Amazon Redshift. Amazon Redshift is the cloud data warehouse of choice for tens of thousands of customers who use it to analyze exabytes of data to gain business insights. With Amazon Redshift, you can query data across your data warehouse, operational data stores, and data lake using standard SQL. You can ... Read More

Feed: Alation. Author: Myles Suer. Published July 28, 2022 The cloud is no longer synonymous with risk. There was a time when most CIOs would never consider putting their crown jewels — AKA customer data and associated analytics — into the cloud. But today, there is a magic quadrant for cloud databases and warehouses comprising more than 20 vendors. And adoption is so significant that many participants have earned notable market capitalization. As enterprises migrate to the cloud, two key questions emerge: What’s driving this change? And what must organizations overcome to succeed at cloud data warehousing? What Are the ... Read More

Feed: AWS Big Data Blog. In this second post of a multi-part series, we share best practices for choosing the optimal Amazon Redshift cluster, data architecture, converting stored procedures, compatible functions and queries widely used for SQL conversions, and recommendations for optimizing the length of data types for table columns. You can check out the first post of this series for guidance on planning, running, and validation of a large-scale data warehouse migration from Greenplum to Amazon Redshift using AWS Schema Conversion Tool (AWS SCT). Choose your optimal Amazon Redshift cluster Amazon Redshift has two types of clusters: provisioned and ... Read More

Feed: AWS Big Data Blog. A data warehouse collects and consolidates data from various sources within your organization. It’s used as a centralized data repository for analytics and business intelligence. When working with on-premises legacy data warehouses, scaling the size of your data warehouse or improving performance can mean purchasing new hardware or adding more powerful hardware. This is often expensive and time-consuming. Running your own on-premises data warehouse also requires hiring database managers, administrators to deal with outages, upgrades, and data access requests. As companies become more data-driven, reliable access to centralized data is increasingly important. As a result, ... Read More

Feed: AWS Architecture Blog. Organizations have been using data warehouse and business intelligence (DWBI) workloads to support business decision making for many years. These workloads are brought to the Amazon Web Services (AWS) platform to utilize the benefit of AWS cloud. However, these workloads are built using multiple vendor tools and technologies, and the customer faces the burden of administrative overhead. This post provides architectural guidance to consolidate multiple DWBI technologies to AWS Managed Services to help reduce the administrative overhead, bring operational ease, and business efficiency. Two scenarios are explored: Upstream transactional databases are already on AWS Upstream transactional ... Read More

Feed: James Serra's Blog. Author: James Serra. With Power BI continuing to get many great new features, including the latest in Datamarts (see my blog Power BI Datamarts), I’m starting to hear customers ask “Can I just build my entire enterprise data warehouse solution in Power BI”? In other words, can I just use Power BI and all its built-in features instead of using Azure Data Lake Gen2, Azure Data Factory (ADF), Azure Synapse, Databricks, etc? The short answer is “No”. First off, as far as Datamarts, I do not recommend using this new capability as a compete or replacement ... Read More

Feed: AWS Big Data Blog. Customers are migrating data warehouses to Amazon Redshift because it’s fast, scalable, and cost-effective. However, data warehouse migration projects can be complex and challenging. In this post, I help you understand the common drivers of data warehouse migration, migration strategies, and what tools and services are available to assist with your migration project. Let’s first discuss the big data landscape, the meaning of a modern data architecture, and what you need to consider for your data warehouse migration project when building a modern data architecture. Business opportunities Data is changing the way we work, live, ... Read More

Feed: AWS Big Data Blog. This is the fifth in a series of posts. We’re excited to share dozens of new features to automate your schema conversion; preserve your investment in existing scripts, reports, and applications; accelerate query performance; and potentially simplify your migrations from legacy data warehouses to Amazon Redshift. Amazon Redshift is the leading cloud data warehouse. No other data warehouse makes it as easy to gain new insights from your data. With Amazon Redshift, you can query exabytes of data across your data warehouse, operational data stores, and data lake using standard SQL. You can also integrate ... Read More

Feed: Actian. Author: Teresa Wingfield. Do you need a Data Warehouse or Enterprise Data Warehouse (EDW) in your organization today? Its functional units, people, services, and other organizational assets support the vision and mission of an organization. An organization can have strategies, tactics, and operational activities that are performed to meet the overall vision and mission of the organization for service to their customers. To be high performing today requires utilizing the power of data supported by innovations in information technology. People and advanced technologies are making decisions every day to support the organization’s success. Data drives decisions.Today, one of ... Read More

Feed: Cloudera Blog. Author: Justin Hayes. Posted in Technical | March 02, 2022 8 min read Apache Impala is used today by over 1,000 customers to power their analytics in on premise as well as cloud-based deployments. Large user communities of analysts and developers benefit from Impala’s fast query execution, helping them get their work done more effectively. For these users performance and concurrency are always top of mind.  An important technique to ensure good performance and concurrency is through efficient usage of memory. If we can make better use of memory, less time is spent with queries queueing up ... Read More