Category: DB2

Feed: Databasejournal.com - Feature Database Articles. Author: . As recently as a decade ago, database administrators had to execute many complex and interesting tasks such as capacity planning, SQL performance tuning and most recently supporting business analytics in the big data environment. Today, however, most of these tasks are performed by artificial intelligence systems or the database itself. With so few important tasks left to perform, are DBAs really needed anymore? The Classic DBA As databases and their supporting software matured in the 1980s, IT departments had to grow or hire support staff to handle a large variety of technical ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . Big data as an application (or as a service) is being supplanted by artificial intelligence (AI) and machine learning. Few new requirements for a big data solution have arisen in the past few years. All the low-hanging fruit (fraud detection, customer preferences, just-in-time re-stocking and delivery, etc.) have already been big data-ized. Is this the end of big data?  Big Data Evolution The first big data applications were for forecasting based on historical data. IT extracted operational data on a regular basis (usually nightly) to store within a stand-alone big data solution ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . Featured Database Articles Posted April 23, 2019 By Marcin Policht Data imports represent some of the more commonly performed database management tasks. When importing data into Azure SQL Database, you can leverage a number of traditional SQL Server data import techniques. In this article, you will get an overview of these techniques and learn about the cloud-specific aspects of importing. The most straightforward methods that you can use in order to import data into tables hosted by an instance of Azure SQL Database include: Transact-SQL statements: You have the option of invoking ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . Artificial Intelligence in IT Today Many IT departments have implemented software solutions that go beyond simple transaction and analytical processing. These packages contain models that describe certain data behaviors, and these models consume current data to see if these patterns of data behavior exist. If so, operational systems can use this information to make decisions. A good example of this is fraud detection. IT data engineers use analytics on historical data to determine when fraud occurred, code this into a model, and deploy the model as a service. Then, any operational system ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . In September 2018, IBM announced a new product, IBM Db2 AI for z/OS. This artificial intelligence engine monitors data access patterns from executing SQL statements, uses machine learning algorithms to decide on optimal patterns and passes this information to the Db2 query optimizer for use by subsequent statements. Machine Learning on the IBM z Platform In May of 2018, IBM announced version 1.2 of its Machine Learning for z/OS (MLz) product. This is a hybrid zServer and cloud software suite that ingests performance data, analyzes and builds models that represent the health ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . The IBM Db2 family includes products for operational databases, data warehouses, data lakes and fast data. The IBM DB2 Relational Database Management System is designed to store, analyze and retrieve the data efficiently. DB2 is extended with the support of Object-Oriented features and non-relational structures with XML. The Data warehouse includes a common SQL engine to support a wide range of data structures and types. IBM Data Lake enables agile, data-driven decisions by utilizing vast amounts of unstructured data that historically could not be analyzed. IBM Fast Data combines fast data ingestion ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . How Analytics Began Business analytics began as the science and art of analyzing large volumes of data to determine trends and correlations that could be used to make business decisions. The origin of analytic processing began with the emergence of the enterprise data warehouse in the late 1990s. The typical warehouse began as an accumulation of regular (usually daily) snapshots of production data that were transformed and cleansed to remove invalid or missing data. While warehouses began as co-located with their operational sources, it soon became obvious that the ever-growing amounts of ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . As big data solutions grow in size and complexity, concerns can rise about future performance. Will the current application scale up and out? Are there potential issues with data consistency? Can your big data application co-exist on the same hardware with regular production processing, or is it destined to be implemented in either a stand-alone configuration or in the cloud? One way to assess the potential effects of these issues is to measure your big data application’s health. In the Beginning Early big data solutions were presented as stand-alone, turnkey systems that ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . Until recently, business analytics against big data and the enterprise data warehouse had to come from sophisticated software packages. This was because many statistical functions such as medians and quartiles were not available in basic SQL, forcing the packages to retrieve large result sets and perform aggregations and statistics locally. Today, many database management systems have incorporated these functions into SQL. This includes IBM's flagship product, Db2. Basic Analytics Many large IT shops implemented big data solutions over a decade ago. At that time, the science of statistics was well-established. Business analysts ... Read More

Feed: Databasejournal.com - Feature Database Articles. Author: . A new separately-priced software offering from IBM on z/OS systems uses machine learning and artificial intelligence to assist the Db2 Optimizer in choosing high-performance access paths for SQL statements. Database Proliferation Affects DBAs At the enterprise level, it is now normal to expect that production applications issue hundreds of thousands (or even millions) of SQL statements every day. These statements run the gamut of complexity from single-row requests based upon a unique key to multi-table joins to business analytical queries involving scores of tables and potentially running for hours. Yet, despite decades ... Read More