This white paper discusses the issues involved in the traditional practice of deploying transactional and analytic applications on separate platforms using separate databases. It analyzes the results from a user survey, conducted on SAP's behalf by IDC, that explores these issues.

Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure 1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data. In-memory databases have helped address p

Traditional backup systems fail to meet the database protection and recovery requirements of modern organizations. These systems require ever-growing backup windows, negatively impact performance in mission-critical production databases, and deliver recovery time objectives (RTO) and recovery point objectives (RPO) measured in hours or even days, failing to meet the requirements of high-volume, high transactional databases -- potentially costing millions in lost productivity and revenue, regulatory penalties, and reputation damage due to an outage or data loss.

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.

Databases have long served as the lifeline of the business. Therefore, it is no surprise that performance has always been top of mind. Whether it be a traditional row-formatted database to handle millions of transactions a day or a columnar database for advanced analytics to help uncover deep insights about the business, the goal is to service all requests as quickly as possible. This is especially true as organizations look to gain an edge on their competition by analyzing data from their transactional (OLTP) database to make more informed business decisions. The traditional model (see Figure 1) for doing this leverages two separate sets of resources, with an ETL being required to transfer the data from the OLTP database to a data warehouse for analysis. Two obvious problems exist with this implementation. First, I/O bottlenecks can quickly arise because the databases reside on disk and second, analysis is constantly being done on stale data. In-memory databases have helped address p

If you’re evaluating big data integration platforms, you know that with the increasing number of tools and technologies out there, it can be difficult to separate meaningful information from the hype, and identify the right technology to solve your unique big data problem. This analyst research provides a concise overview of big data integration technologies, and reviews key things to consider when creating an integrated big data environment that blends new technologies with existing BI systems to meet your business goals. Read the Buyer’s Guide to Big Data Integration by CITO Research to learn: • What tools are most useful for working with Big Data, Hadoop, and existing transactional databases • How to create an effective “data supply chain” • How to succeed with complex data on-boarding using automation for more reliable data ingestion • The best ways to connect, transport, and transform data for data exploration, analytics and compliance