- Data Warehouse Concepts, Design, and Data Integration
- Data Warehouse
- Data Integration
- Data Integration in Data Mining
- Data warehouse
Data Warehouse Concepts, Design, and Data IntegrationAn enterprise uses an average of applicationsalong with many other on-premise systems. This means that you can have about a thousand source systems from different vendors, each storing data differently. When data is scattered across so many systems throughout the enterprise, how do you make sense out of it? If you have a little know-how of the data management realm, you already know that data integration is the answer. The simplest and the earliest data integration approach, it refers to exporting data from a source system in a file and then importing it to your target system. You could export data from individual campaigns in a. CSV file and import it to your sales application manually. The other option is to develop a custom program that automatically exports data from specified campaigns and imports it at a pre-configured time. Perhaps your marketing system has separate fields for FirstName and LastName while the sales app only has the FullName field. Another major limitation is that you can only export and import data to and from two systems at a time. In enterprise environments, you could potentially be required to integrate data from hundreds of applications. Once you start thinking about data integration on a large scale, ETL becomes an viable option, one that has been around for decades due to its utility and scalability. As is clear from the abbreviation, the ETL process revolves around Extracting the desired data from the source system, Transforming to blend and convert it into a consistent format, and finally Loading it to the target system. The entire process is largely automated, with modern tools offering a workflow creation utility where you can specify the source and destination systems, define transformations and business rules to be applied, and configure how you want the data to be read and written. The workflow could include multiple integrations from a variety of source systems. Once completed, you can execute the workflow to run ETL jobs behind the scenes. While ETL does have its own set of challenges, many of them are not properly understood. Take your workflows deeper, and could even create an Enterprise Data Warehouse or data marts if you start thinking of your integration flows on a macro level. Another ETL misconception is that it only allows data to be loaded in batches, on fixed hourly, daily, or weekly frequencies. While ETL has been around since the 70s, point-to-point integrations remained popular until the s when the increasing number of enterprise applications made the approach unsustainable. This is clearly impractical, more so when you account for maintenance. This model centers on a hub-and-spoke approach to build point-to-point integrations. ESB software offer a pre-built environment that allows rapid development of point-to-point connections in an enterprise while allowing the capability to develop transformations, error-handling, and performance metrics within that same environment. The result is an integrated layer of services, with business intelligence applications invoking services from the primary layer. This solution has made point-to-point integrations viable again for complex integrations, but still requires IT involvement. The data virtualization approach is becoming increasingly popular because it eliminates physical data movement altogether. Data sits where it is created in source systems, and queries are run on a virtualization layer that insulates users from the technical details of accessing data. Queries could be from your reporting application, or any business intelligence system that retrieves data, blends it, and displays results to users. For the connecting applications, the virtualization layer looks like a single, consolidated database, but in reality, data is accessed from different source systems. Data virtualization software today also support caching mechanisms, so you can make it available when running multiple queries on the same sets of data, which reduces time and effort. Rather, it complements the data warehouse by providing convenient access to unstructured data types. Ideally, your chosen solution should support multiple data integration types for building integrations as per your business needs and have supporting automation features to ramp up the process. Your email address will not be published. Yes, add me to your mailing list. This site uses functional cookies and external scripts to improve your experience. Which cookies and scripts are used and how they impact your visit is specified on the left. You may change your settings at any time. Your choices will not impact your visit. NOTE: These settings will only apply to the browser and device you are currently using. Search for: Search.
Having a discussion about data integration might seem simple enough. However the term can be interpreted quite differently, depending on the context. Data integration in the purest sense is about carefully and methodically blending data from different sources, making it more useful and valuable than it was before. There are methods of bringing data together into an integrated view and there are techniques for bringing data together physically, for an integration version. Below are a few common data integration approaches. Data consolidation physically brings data together from several separate systems, creating a version of the consolidated data in one data store. Often the goal of data consolidation is to reduce the number of data storage locations. Extract, transform, and load ETL technology supports data consolidation. ETL pulls data from sources, transforms it into an understandable format, and then transfers it to another database or data warehouse. The ETL process cleans, filters, and transforms data, and then applies business rules before data populates the new source. Data propagation is the use of applications to copy data from one location to another. It is event-driven and can be done synchronously or asynchronously. Most synchronous data propagation supports a two-way data exchange between the source and the target. EAI integrates application systems for the exchange of messages and transactions. It is often used for real-time business transaction processing. EDR typically transfers large amounts of data between databases, instead of applications. Virtualization uses an interface to provide a near real-time, unified view of data from disparate sources with different data models. Data can be viewed in one location, but is not stored in that single location. Data virtualization retrieves and interprets data, but does not require uniform formatting or a single point of access. Federation is technically a form of data virtualization.
In computinga data warehouse DW or DWHalso known as an enterprise data warehouse EDWis a system used for reporting and data analysisand is considered a core component of business intelligence. They store current and historical data in one single place  that are used for creating analytical reports for workers throughout the enterprise. The data stored in the warehouse is uploaded from the operational systems such as marketing or sales. The data may pass through an operational data store and may require data cleansing  for additional operations to ensure data quality before it is used in the DW for reporting. Extract, transform, load ETL and extract, load, transform E-LT are the two main approaches used to build a data warehouse system. The typical extract, transform, load ETL -based data warehouse  uses stagingdata integrationand access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store ODS database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data. The main source of the data is cleansedtransformed, catalogued, and made available for use by managers and other business professionals for data miningonline analytical processingmarket research and decision support. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence toolstools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. Instead, it maintains a staging area inside the data warehouse itself. In this approach, data gets extracted from heterogeneous source systems and are then directly loaded into the data warehouse, before any transformation occurs. All necessary transformations are then handled inside the data warehouse itself. Finally, the manipulated data gets loaded into target tables in the same data warehouse. A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to:. In regards to source systems listed above, R. Kelly Rainer states, "A common source for the data in data warehouses is the company's operational databases, which can be relational databases". Regarding data integration, Rainer states, "It is necessary to extract data from source systems, transform them, and load them into a data mart or warehouse". Rainer discusses storing data in an organization's data warehouse or data marts. Metadata is data about data. Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A key to this response is the effective and efficient use of data and information by analysts and managers. A data mart is a simple form of a data warehouse that is focused on a single subject or functional areahence they draw data from a limited number of sources such as sales, finance or marketing. Data marts are often built and controlled by a single department within an organization. The sources could be internal operational systems, a central data warehouse, or external data. Given that data marts generally cover only a subset of the data contained in a data warehouse, they are often easier and faster to implement. Types of data marts include dependentindependent, and hybrid data marts. Online analytical processing OLAP is characterized by a relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems, response time is an effectiveness measure. OLAP databases store aggregated, historical data in multi-dimensional schemas usually star schemas. OLAP systems typically have data latency of a few hours, as opposed to data marts, where latency is expected to be closer to one day. The OLAP approach is used to analyze multidimensional data from multiple sources and perspectives.
Data Integration in Data Mining