Data Warehousing 101 Concepts And Implementation Pdf
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- Data Warehouse Architecture, Concepts and Components
- Data Warehouse Architecture, Concepts and Components
- Data warehousing 101 concepts and implementation pdf merge
Same thing with Amazon, see Note 1 below. I was sometimes asked by people who wanted to learn data warehousing to recommend a book for them.
Data Warehouse Architecture, Concepts and Components
The basic concept of a Data Warehouse is to facilitate a single version of truth for a company for decision making and forecasting. A Data warehouse is an information system that contains historical and commutative data from single or multiple sources. Data Warehouse Concepts simplify the reporting and analysis process of organizations.
These subjects can be sales, marketing, distributions, etc. A data warehouse never focuses on the ongoing operations. Instead, it put emphasis on modeling and analysis of data for decision making. It also provides a simple and concise view around the specific subject by excluding data which not helpful to support the decision process.
Integrated In Data Warehouse, integration means the establishment of a common unit of measure for all similar data from the dissimilar database.
The data also needs to be stored in the Datawarehouse in common and universally acceptable manner. A data warehouse is developed by integrating data from varied sources like a mainframe, relational databases, flat files, etc.
Moreover, it must keep consistent naming conventions, format, and coding. This integration helps in effective analysis of data. Consistency in naming conventions, attribute measures, encoding structure etc. Consider the following example: In the above example, there are three different application labeled A, B and C. Information stored in these applications are Gender, Date, and Balance. However, each application's data is stored different way. In Application A gender field store logical values like M or F In Application B gender field is a numerical value, In Application C application, gender field stored in the form of a character value.
Same is the case with Date and balance However, after transformation and cleaning process all this data is stored in common format in the Data Warehouse.
Time-Variant The time horizon for data warehouse is quite extensive compared with operational systems. The data collected in a data warehouse is recognized with a particular period and offers information from the historical point of view.
It contains an element of time, explicitly or implicitly. One such place where Datawarehouse data display time variance is in in the structure of the record key. Every primary key contained with the DW should have either implicitly or explicitly an element of time. Like the day, week month, etc. Another aspect of time variance is that once data is inserted in the warehouse, it can't be updated or changed.
Non-volatile Data warehouse is also non-volatile means the previous data is not erased when new data is entered in it. Data is read-only and periodically refreshed.
It does not require transaction process, recovery and concurrency control mechanisms. Activities like delete, update, and insert which are performed in an operational application environment are omitted in Data warehouse environment. Only two types of data operations performed in the Data Warehousing are Data loading Data access Here, are some major differences between Application and Data Warehouse Operational Application Data Warehouse Complex program must be coded to make sure that data upgrade processes maintain high integrity of the final product.
This kind of issues does not happen because data update is not performed. Data is placed in a normalized form to ensure minimal redundancy. Data is not stored in normalized form.
Technology needed to support issues of transactions, data recovery, rollback, and resolution as its deadlock is quite complex. It offers relative simplicity in technology. This 3 tier architecture of Data Warehouse is explained as below. Single-tier architecture The objective of a single layer is to minimize the amount of data stored. This goal is to remove data redundancy.
This architecture is not frequently used in practice. Two-tier architecture Two-layer architecture is one of the Data Warehouse layers which separates physically available sources and data warehouse. This architecture is not expandable and also not supporting a large number of end-users.
It also has connectivity problems because of network limitations. It consists of the Top, Middle and Bottom Tier. Bottom Tier: The database of the Datawarehouse servers as the bottom tier. It is usually a relational database system. Data is cleansed, transformed, and loaded into this layer using back-end tools.
For a user, this application tier presents an abstracted view of the database. This layer also acts as a mediator between the end-user and the database. Top-Tier: The top tier is a front-end client layer. Top tier is the tools and API that you connect and get data out from the data warehouse. It could be Query tools, reporting tools, managed query tools, Analysis tools and Data mining tools.
Datawarehouse Components We will learn about the Datawarehouse Components and Architecture of Data Warehouse with Diagram as shown below: Data Warehouse Architecture The Data Warehouse is based on an RDBMS server which is a central information repository that is surrounded by some key Data Warehousing components to make the entire environment functional, manageable and accessible. There are mainly five Data Warehouse Components: Data Warehouse Database The central database is the foundation of the data warehousing environment.
Although, this kind of implementation is constrained by the fact that traditional RDBMS system is optimized for transactional database processing and not for data warehousing. For instance, ad-hoc query, multi-table joins, aggregates are resource intensive and slow down performance.
Hence, alternative approaches to Database are used as listed below- In a datawarehouse, relational databases are deployed in parallel to allow for scalability. Parallel relational databases also allow shared memory or shared nothing model on various multiprocessor configurations or massively parallel processors. New index structures are used to bypass relational table scan and improve speed.
Example: Essbase from Oracle. Sourcing, Acquisition, Clean-up and Transformation Tools ETL The data sourcing, transformation, and migration tools are used for performing all the conversions, summarizations, and all the changes needed to transform data into a unified format in the datawarehouse. Their functionality includes: Anonymize data as per regulatory stipulations.
Eliminating unwanted data in operational databases from loading into Data warehouse. Search and replace common names and definitions for data arriving from different sources. Calculating summaries and derived data In case of missing data, populate them with defaults. De-duplicated repeated data arriving from multiple datasources. These Extract, Transform, and Load tools may generate cron jobs, background jobs, Cobol programs, shell scripts, etc. These tools are also helpful to maintain the Metadata.
However, it is quite simple. Metadata is data about data which defines the data warehouse. It is used for building, maintaining and managing the data warehouse. In the Data Warehouse Architecture, meta-data plays an important role as it specifies the source, usage, values, and features of data warehouse data. It also defines how data can be changed and processed. It is closely connected to the data warehouse. For example, a line in sales database may contain: KJ Metadata helps to answer the following questions What tables, attributes, and keys does the Data Warehouse contain?
Where did the data come from? How many times do data get reloaded? What transformations were applied with cleansing? Metadata can be classified into following categories: Technical Meta Data : This kind of Metadata contains information about warehouse which is used by Data warehouse designers and administrators. Business Meta Data: This kind of Metadata contains detail that gives end-users a way easy to understand information stored in the data warehouse. Query Tools One of the primary objects of data warehousing is to provide information to businesses to make strategic decisions.
Query tools allow users to interact with the data warehouse system. Query and reporting tools: Query and reporting tools can be further divided into Reporting tools Managed query tools Reporting tools: Reporting tools can be further divided into production reporting tools and desktop report writer. Report writers: This kind of reporting tool are tools designed for end-users for their analysis. Production reporting: This kind of tools allows organizations to generate regular operational reports.
It also supports high volume batch jobs like printing and calculating. Managed query tools: This kind of access tools helps end users to resolve snags in database and SQL and database structure by inserting meta-layer between users and database. Application development tools: Sometimes built-in graphical and analytical tools do not satisfy the analytical needs of an organization.
In such cases, custom reports are developed using Application development tools. Data mining tools: Data mining is a process of discovering meaningful new correlation, pattens, and trends by mining large amount data.
Data mining tools are used to make this process automatic. OLAP tools: These tools are based on concepts of a multidimensional database. It allows users to analyse the data using elaborate and complex multidimensional views.
Data warehouse Bus Architecture Data warehouse Bus determines the flow of data in your warehouse. The data flow in a data warehouse can be categorized as Inflow, Upflow, Downflow, Outflow and Meta flow. While designing a Data Bus, one needs to consider the shared dimensions, facts across data marts. Data Marts A data mart is an access layer which is used to get data out to the users. It is presented as an option for large size data warehouse as it takes less time and money to build.
However, there is no standard definition of a data mart is differing from person to person.
Data Warehouse Architecture, Concepts and Components
Accelerate data integration with more than 30 native data connectors from azure data factory and support for leading information management tools from. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured andor ad hoc queries, and decision making. Concepts and implementation will appeal to those planning data warehouse projects, senior executives, project managers, and project implementation team members. Data that gives information about a particular subject instead of about a companys ongoing operations. Import big data with simple polybase tsql queries, and.
As organizations develop, migrate, or consolidate data warehouses, they must employ best practices for data warehouse testing. The success of any on-premise or cloud data warehouse solution depends on the execution of valid test cases that identify issues related to data quality. Extract, Transform, and Load ETL is the common process used to load data from source systems to the data warehouse. Data is extracted from the source, transformed to match the target schema, and loaded into the data warehouse. ETL testing ensures that the transformation of data from source to warehouse is accurate. It also involves verifying data at each point between the source and destination. This article will focus on the traditional ETL testing process.
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Data warehousing 101 concepts and implementation pdf merge
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We are living in the age of a data revolution, and more corporations are realizing that to lead—or in some cases, to survive—they need to harness their data wealth effectively. The data warehouse, due to its unique proposition as the integrated enterprise repository of data, is playing an even more important role in this situation.