Treasure Data is going to change the way that you think about Big Data and Cloud Data Warehousing. We'd like to get your input on how you see Big Data and Cloud Data Warehousing. Please take our 10 question survey and give us your input.
Compare IBM DB2 pureScale with any other offering being considered for implementing a clustered, scalable database configuration see how they deliver continuous availability and why they are important. Download now!
DB2 is a proven database for handling the most demanding transactional workloads. But the trend as of
late is to enable relational databases to handle analytic queries more efficiently by adding an inmemory
column store alongside to aggregate data and provide faster results. IBM's BLU Acceleration
technology does exactly that. While BLU isn't brand new, the ability to spread the column store across
a massively parallel processing (MPP) cluster of up to 1,000 nodes is a new addition to the technology.
That, along with simpler monthly pricing options and integration with dashDB data warehousing in the
cloud, makes DB2 for LUW, a very versatile database.
Read this white paper to discover how predictive analytics and cognitive commerce make it possible to get instant access to integrated information and actionable insights so you can deliver superior-and profitable-interactions with customers. You'll learn: What it takes to uncover hidden trends and explore relationships across disparate data sources using natural language queries Ways to use in-depth insight to create highly relevant campaigns and content that's aligned with individual customer behaviors and preferences How to take product recommendations to new levels of accuracy with pinpoint prediction and targeting.
To address the volume, velocity, and variety of data necessary for population health management, healthcare organizations need a big data solution that can integrate with other technologies to optimize care management, care coordination, risk identification and stratification and patient engagement. Read this whitepaper and discover how to build a data infrastructure using the right combination of data sources, a “data lake” framework with massively parallel computing that expedites the answering of queries and the generation of reports to support care teams, analytic tools that identify care gaps and rising risk, predictive modeling, and effective screening mechanisms that quickly find relevant data. In addition to learning about these crucial tools for making your organization’s data infrastructure robust, scalable, and flexible, get valuable information about big data developments such as natural language processing and geographical information systems. Such tools can provide insig
Amazon Redshift Spectrum—a single service that can be used in conjunction with other Amazon services and products, as well as external tools—is revolutionizing the way data is stored and queried, allowing for more complex analyses and better decision making.
Spectrum allows users to query very large datasets on S3 without having to load them into Amazon Redshift. This helps address the Scalability Dilemma—with Spectrum, data storage can keep growing on S3 and still be processed. By utilizing its own compute power and memory, Spectrum handles the hard work that would
normally be done by Amazon Redshift. With this service, users can now scale to accommodate larger amounts of data than the cluster would have been capable of processing with its own resources.
This e-book aims to provide you with expert tips on how to
use Amazon Redshift Spectrum to increase performance
and potentially reduce the cost of your queries.
Businesses are generating staggering amounts of data—and extracting the most value from this information is paramount. Amazon Redshift provides organizations what they’re looking for: Affordability and flexibility combined with a powerful feature set.
Download our solution overview covering some of the best practices on loading data and making the most of Amazon Redshift, including:
• Loading data for faster results
• Querying data for gaining actionable insights
• Creating a schema to forgo complicated queries, saving time
With the growth of unstructured data and the challenges of modern workloads such as Apache Spark™, IT teams have seen a clear need during the past few years for a new type of all-flash storage solution, one that has been designed specifically for users requiring high levels of performance in file- and object-based environments. With FlashBlade™, it addresses performance challenges in Spark environments by delivering the consistent performance of all-flash storage with no caching or tiering, as well as fast metadata operations and instant metadata queries.
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics.
Amazon Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set:
• Enterprise-class relational database query and management system
• Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools
• Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
Published By: Vertica
Published Date: Aug 16, 2010
The Vertica Analytic Database is the only database built from scratch to handle today's heavy business intelligence workloads. In customer benchmarks, Vertica has been shown to manage terabytes of data running on extraordinarily low-cost hardware and answers queries 50 to 200 times faster than competing row-oriented databases and specialized analytic hardware. This document summarizes the key aspects of Vertica's technology that enable such dramatic performance benefits, and compares the design of Vertica to other popular relational systems.
Relational database management systems (RDBMSs) are systems of software that manage databases as structured sets of tables containing rows and columns with references to one another through key values. They include the ability to optimize storage, process transactions, perform queries, and preserve the integrity of data structures. When used with applications, they provide the beating heart of the collection of business functions supported by those applications. They vary considerably in terms of the factors that impact the total cost of running a database application, yet users seldom perform a disciplined procedure to calculate such costs. Most users choose instead to remain with a single vendor's RDBMS and never visit the question of ongoing hardware, software, and staffing fees.
Read this white paper to discover how predictive analytics and cognitive commerce make it possible to get instant access to integrated information and actionable insights so you can deliver superior-and profitable-interactions with customers. You'll learn: What it takes to uncover hidden trends and explore relationships across disparate data sources using natural language queries Ways to use in-depth insight to create highly relevant campaigns and content that's aligned with individual customer behaviors and preferences How to take product recommendations to new levels of accuracy with pinpoint prediction and targeting
If you specialize in relational database management technology, you’ve probably heard a lot about “big data” and the open source Apache Hadoop project. Perhaps you’ve also heard about IBM’s new Big SQL technology, which enables IBM® InfoSphere® BigInsights™ users to query Hadoop data using industry-standard SQL. Curious? This paper introduces you to Big SQL, answering many of the common questions that relational database management system (DBMS) users have about this IBM technology.
Born from new advances in data processing from IBM Research, IBM® DB2® with BLU Acceleration is a leap forward in database technology that raises the bar for performance and value. BLU Acceleration uses patented technologies to deliver a unique combination of performance, ease of use and cost-efficiency—with 8 to 25 times faster reporting and analytics1 and cases of more than 1,000 times faster answers to queries.2 BLU Acceleration also complements in-memory Dynamic Cubes in IBM Cognos® Business Intelligence with 24 times faster query performance.
WinterCorp analyzes IBM's DB2 Warehouse and how it addresses twin challenges facing enterprises today: improving the value derived from the torrents of information processed every day, while lowering costs at the same time. Discover why WinterCorp believes the advances in data clustering strategies and intelligent software compression algorithms in IBM's Data Warehouse improves performance of business intelligence queries by radically reducing the I/O's needed to resolve them.