The document provides explanations of various SQL concepts including cross join, order by, distinct, union and union all, truncate and delete, compute clause, data warehousing, data marts, fact and dimension tables, snowflake schema, ETL processing, BCP, DTS, multidimensional analysis, and bulk insert. It also discusses the three primary ways of storing information in OLAP: MOLAP, ROLAP, and HOLAP.
Data Warehouse Physical Design,Physical Data Model, Tablespaces, Integrity Constraints, ETL (Extract-Transform-Load) ,OLAP Server Architectures, MOLAP vs. ROLAP, Distributed Data Warehouse ,
A database is an organized collection of data stored in tables. A database management system (DBMS) like Oracle or SQL Server is used to create and manage databases. Data is stored in tables which are organized into rows and columns. Structured Query Language (SQL) allows users to retrieve, insert, update, and delete data from the database using commands like SELECT, INSERT, UPDATE, and DELETE. Database concepts like tables, queries, and the client-server model allow for effective data storage and manipulation.
An introduction to database architecture, design and development, its relation to Object Oriented Analysis & Design in software, Illustration with examples to database normalization and finally, a basic SQL guide and best practices
This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
After completing this module, you will be able to:
List and describe the major components of the Teradata architecture.
Describe how the components interact to manage incoming and outgoing data.
List 5 types of Teradata database objects.
The document discusses dimensional modeling concepts used in data warehouse design. Dimensional modeling organizes data into facts and dimensions. Facts are measures that are analyzed, while dimensions provide context for the facts. The dimensional model uses star and snowflake schemas to store data in denormalized tables optimized for querying. Key aspects covered include fact and dimension tables, slowly changing dimensions, and handling many-to-many and recursive relationships.
Etl Overview (Extract, Transform, And Load)LizLavaveshkul
The document provides an overview of IBM Ascential ETL tools DataStage and QualityStage. QualityStage is used for data cleansing and standardization tasks like investigating data patterns, standardizing formats, matching records, and determining which data survives from source to target. DataStage is used for data transformation and movement between source systems and targets through jobs with stages for extraction, transformation and loading.
Data Warehouse Physical Design,Physical Data Model, Tablespaces, Integrity Constraints, ETL (Extract-Transform-Load) ,OLAP Server Architectures, MOLAP vs. ROLAP, Distributed Data Warehouse ,
A database is an organized collection of data stored in tables. A database management system (DBMS) like Oracle or SQL Server is used to create and manage databases. Data is stored in tables which are organized into rows and columns. Structured Query Language (SQL) allows users to retrieve, insert, update, and delete data from the database using commands like SELECT, INSERT, UPDATE, and DELETE. Database concepts like tables, queries, and the client-server model allow for effective data storage and manipulation.
An introduction to database architecture, design and development, its relation to Object Oriented Analysis & Design in software, Illustration with examples to database normalization and finally, a basic SQL guide and best practices
This document discusses various concepts in data warehouse logical design including data marts, types of data marts (dependent, independent, hybrid), star schemas, snowflake schemas, and fact constellation schemas. It defines each concept and provides examples to illustrate them. Dependent data marts are created from an existing data warehouse, independent data marts are stand-alone without a data warehouse, and hybrid data marts combine data from a warehouse and other sources. Star schemas have one table for each dimension that joins to a central fact table, while snowflake schemas have normalized dimension tables. Fact constellation schemas have multiple fact tables that share dimension tables.
After completing this module, you will be able to:
List and describe the major components of the Teradata architecture.
Describe how the components interact to manage incoming and outgoing data.
List 5 types of Teradata database objects.
The document discusses dimensional modeling concepts used in data warehouse design. Dimensional modeling organizes data into facts and dimensions. Facts are measures that are analyzed, while dimensions provide context for the facts. The dimensional model uses star and snowflake schemas to store data in denormalized tables optimized for querying. Key aspects covered include fact and dimension tables, slowly changing dimensions, and handling many-to-many and recursive relationships.
Etl Overview (Extract, Transform, And Load)LizLavaveshkul
The document provides an overview of IBM Ascential ETL tools DataStage and QualityStage. QualityStage is used for data cleansing and standardization tasks like investigating data patterns, standardizing formats, matching records, and determining which data survives from source to target. DataStage is used for data transformation and movement between source systems and targets through jobs with stages for extraction, transformation and loading.
The document provides an overview of SQL (Structured Query Language) including its purpose, benefits, and key components. It describes the SQL environment and data types, as well as the main SQL statements used for database definition (DDL), data manipulation (DML), and control (DCL). Examples are given for common statements like CREATE TABLE, SELECT, INSERT, UPDATE, DELETE, and how to define views, integrity controls, indexes and more.
This document provides an introduction to relational databases and database concepts. It defines key terms like table, row, attribute, and relational database management system. It also describes how to manage databases using SQL statements to create, insert, update, delete, and query data. Specific SQL statements are shown for creating databases and tables, joining data, and using aggregation functions. Entity-relationship modeling is introduced as a technique for database design.
The document discusses key aspects of the ETL (extraction, transformation, and loading) process used to update data warehouses. It describes the two main strategies for building a data warehouse - the enterprise-wide top-down approach and the bottom-up data mart approach. The document also outlines the major steps in ETL including data extraction, transformation, data staging, data cleansing, data loading, and managing metadata.
SALES BASED DATA EXTRACTION FOR BUSINESS INTELLIGENCEcscpconf
Data warehouse use has increased significantly in recent years and now plays a fundamental role in many organizations’ decision-support processes. An effective business intelligence
infrastructure that leverages the power of a data warehouse can deliver value by helping companies enhance their customer experience. In this paper is to generate reports with various
drilldowns and slier conditions with suitable parameters which provide a complete business solution which is helpful for monitor the company inflow and outflow. The goal of the work is
for potential users of the data warehouse in their decision making process in the Business process system to get a complete visual effort of those reports by creating the chart and grid interface from warehouse. The example in this paper relate directly to the Adventure Work Data Warehouse Project implementation which helps to know the internet sales amount according to different date
Sas dataflux management studio Training ,data flux corporate trainig bidwhm
The document discusses outsourcing resources, technical support, training and installation/admin support for DataFlux Data Management Platform. It provides an overview of DataFlux Data Management Studio and the DataFlux methodology of planning, acting and monitoring. It also outlines various functions within the platform like managing repositories, data connections, data collections, data explorations, business rules, data profiling, data jobs, expression engine language and more.
The document discusses data warehousing concepts and technologies. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used for decision making. Key aspects covered include multidimensional data modeling using facts, dimensions, and cubes; data warehouse architectures; and efficient cube computation methods such as ROLAP-based algorithms.
This document provides an introduction to database management systems (DBMS). It discusses what a DBMS is, common database applications, and drawbacks of using file systems to store data that DBMS aim to address, such as data redundancy, integrity issues, and concurrent access problems. It also summarizes key components of a DBMS, including its logical and physical levels of abstraction, data models, data definition and manipulation languages, storage management, query processing, transaction management, and common database architectures.
The document discusses the key functions of ETL (extract, transform, load) processes which are important for reshaping relevant data from source systems into useful information stored in a data warehouse. It examines the challenges and techniques for data extraction and the wide range of transformation tasks. It also explains that ETL encompasses extracting data from source systems, transforming it into appropriate formats for the data warehouse, and loading it into the data warehouse repository.
The document provides an overview of using SQL to query relational databases, logical modeling to create relational databases, and querying multitable databases. It also discusses using XML for data transfer.
Specifically, it covers: using SQL to query single and multitable databases; logical modeling using entity-relationship diagrams; converting entity-relationship diagrams into relational data models; and performing JOIN operations to query relationships across multiple tables.
The document discusses multidimensional databases and data warehousing. It describes multidimensional databases as optimized for data warehousing and online analytical processing to enable interactive analysis of large amounts of data for decision making. It discusses key concepts like data cubes, dimensions, measures, and common data warehouse schemas including star schema, snowflake schema, and fact constellations.
The document discusses MySQL, an open-source relational database management system (RDBMS), including its history and capabilities. It introduces SQL commands for manipulating and retrieving data from MySQL databases, such as SELECT, INSERT, UPDATE, DELETE, and explains operators, functions and clauses used in SQL queries. Key features of MySQL like data definition, manipulation, security and integrity, and transaction control are also summarized.
The document discusses various data modeling techniques for data warehouses including star schemas and column-oriented storage. It notes that traditional OLTP systems are not optimized for data warehousing queries. Star schemas organize data around a central fact table linked to dimension tables and are widely used. However, star schemas can have performance issues like large intermediate results. Column-oriented storage improves performance by storing columns together rather than rows.
This document provides an overview of data preprocessing techniques for data mining. It discusses data quality issues like accuracy, completeness, and consistency that require data cleaning. The major tasks of data preprocessing are described as data cleaning, integration, reduction, and transformation. Specific techniques for handling missing data, noisy data, and reducing redundancy during data integration are also summarized.
Data warehousing and online analytical processingVijayasankariS
The document discusses data warehousing and online analytical processing (OLAP). It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data used to support management decision making. It describes key concepts such as data warehouse modeling using data cubes and dimensions, extraction, transformation and loading of data, and common OLAP operations. The document also provides examples of star schemas and how they are used to model data warehouses.
The document provides an introduction to databases and SQL. It defines what a database is as a collection of related data containing information relevant to an enterprise. It then discusses the properties of databases, what a database management system (DBMS) is, the typical functionality of a DBMS including defining, constructing, manipulating databases, and providing security. It also summarizes the components of a database system including fields, records, queries, and reports. The document then introduces SQL and its uses for data manipulation, definition, and administration. It provides examples of SQL statements for creating tables, inserting, querying, updating, and deleting data.
This document describes a simulator for database aggregation using metadata. The simulator sits between an end-user application and a database management system (DBMS) to intercept SQL queries and transform them to take advantage of available aggregates using metadata describing the data warehouse schema. The simulator provides performance gains by optimizing queries to use appropriate aggregate tables. It was found to improve performance over previous aggregate navigators by making fewer calls to system tables through the use of metadata mappings. Experimental results showed the simulator solved queries faster than alternative approaches by transforming queries to leverage aggregate tables.
The document discusses trends in data mining research, including mining complex data types like sequences, time series, graphs and networks. It covers various data mining methodologies like statistical data mining, visual data mining and views on the foundations of data mining. Statistical techniques discussed include regression, generalized linear models and discriminant analysis. Visual data mining involves using visualization to gain insights from large datasets and present data mining results.
1) The data dictionary is a virtual database that contains metadata (data about data) such as the definition of tables, fields, domains and other database objects. It provides information for data manipulation and processing.
2) Key data dictionary objects include domains, which define field attributes like type and length; data elements, which define field semantics; and tables, which store records of data. Transparent tables can be created to store custom tables.
3) System fields store system-related data like date and time, while structures store temporary data during runtime and views combine data from multiple tables.
Operational database systems are designed to support transaction processing while data warehouses are designed to support analytical processing and report generation. Operational systems focus on business processes, contain current data, and are optimized for fast updates. Data warehouses are subject-oriented, contain historical data that is rarely changed, and are optimized for fast data retrieval. The three main components of a data warehouse architecture are the database server, OLAP server, and client tools. Data is extracted from operational systems, transformed, cleansed, and loaded into fact and dimension tables in the data warehouse using the ETL process. Multidimensional schemas like star, snowflake, and constellation organize this data. Common OLAP operations performed on the data include roll-up,
Data warehousing interview_questionsandanswersSourav Singh
A data warehouse is a repository of integrated data from multiple sources that is organized for analysis. It contains historical data to support decision making. There are four fundamental stages of data warehousing: offline operational databases, offline data warehouse, real-time data warehouse, and integrated data warehouse. Dimensional modeling involves facts tables containing measurements and dimension tables containing context for the measurements. (191 words)
The document provides an overview of SQL (Structured Query Language) including its purpose, benefits, and key components. It describes the SQL environment and data types, as well as the main SQL statements used for database definition (DDL), data manipulation (DML), and control (DCL). Examples are given for common statements like CREATE TABLE, SELECT, INSERT, UPDATE, DELETE, and how to define views, integrity controls, indexes and more.
This document provides an introduction to relational databases and database concepts. It defines key terms like table, row, attribute, and relational database management system. It also describes how to manage databases using SQL statements to create, insert, update, delete, and query data. Specific SQL statements are shown for creating databases and tables, joining data, and using aggregation functions. Entity-relationship modeling is introduced as a technique for database design.
The document discusses key aspects of the ETL (extraction, transformation, and loading) process used to update data warehouses. It describes the two main strategies for building a data warehouse - the enterprise-wide top-down approach and the bottom-up data mart approach. The document also outlines the major steps in ETL including data extraction, transformation, data staging, data cleansing, data loading, and managing metadata.
SALES BASED DATA EXTRACTION FOR BUSINESS INTELLIGENCEcscpconf
Data warehouse use has increased significantly in recent years and now plays a fundamental role in many organizations’ decision-support processes. An effective business intelligence
infrastructure that leverages the power of a data warehouse can deliver value by helping companies enhance their customer experience. In this paper is to generate reports with various
drilldowns and slier conditions with suitable parameters which provide a complete business solution which is helpful for monitor the company inflow and outflow. The goal of the work is
for potential users of the data warehouse in their decision making process in the Business process system to get a complete visual effort of those reports by creating the chart and grid interface from warehouse. The example in this paper relate directly to the Adventure Work Data Warehouse Project implementation which helps to know the internet sales amount according to different date
Sas dataflux management studio Training ,data flux corporate trainig bidwhm
The document discusses outsourcing resources, technical support, training and installation/admin support for DataFlux Data Management Platform. It provides an overview of DataFlux Data Management Studio and the DataFlux methodology of planning, acting and monitoring. It also outlines various functions within the platform like managing repositories, data connections, data collections, data explorations, business rules, data profiling, data jobs, expression engine language and more.
The document discusses data warehousing concepts and technologies. It defines a data warehouse as a subject-oriented, integrated, time-variant, and non-volatile collection of data used for decision making. Key aspects covered include multidimensional data modeling using facts, dimensions, and cubes; data warehouse architectures; and efficient cube computation methods such as ROLAP-based algorithms.
This document provides an introduction to database management systems (DBMS). It discusses what a DBMS is, common database applications, and drawbacks of using file systems to store data that DBMS aim to address, such as data redundancy, integrity issues, and concurrent access problems. It also summarizes key components of a DBMS, including its logical and physical levels of abstraction, data models, data definition and manipulation languages, storage management, query processing, transaction management, and common database architectures.
The document discusses the key functions of ETL (extract, transform, load) processes which are important for reshaping relevant data from source systems into useful information stored in a data warehouse. It examines the challenges and techniques for data extraction and the wide range of transformation tasks. It also explains that ETL encompasses extracting data from source systems, transforming it into appropriate formats for the data warehouse, and loading it into the data warehouse repository.
The document provides an overview of using SQL to query relational databases, logical modeling to create relational databases, and querying multitable databases. It also discusses using XML for data transfer.
Specifically, it covers: using SQL to query single and multitable databases; logical modeling using entity-relationship diagrams; converting entity-relationship diagrams into relational data models; and performing JOIN operations to query relationships across multiple tables.
The document discusses multidimensional databases and data warehousing. It describes multidimensional databases as optimized for data warehousing and online analytical processing to enable interactive analysis of large amounts of data for decision making. It discusses key concepts like data cubes, dimensions, measures, and common data warehouse schemas including star schema, snowflake schema, and fact constellations.
The document discusses MySQL, an open-source relational database management system (RDBMS), including its history and capabilities. It introduces SQL commands for manipulating and retrieving data from MySQL databases, such as SELECT, INSERT, UPDATE, DELETE, and explains operators, functions and clauses used in SQL queries. Key features of MySQL like data definition, manipulation, security and integrity, and transaction control are also summarized.
The document discusses various data modeling techniques for data warehouses including star schemas and column-oriented storage. It notes that traditional OLTP systems are not optimized for data warehousing queries. Star schemas organize data around a central fact table linked to dimension tables and are widely used. However, star schemas can have performance issues like large intermediate results. Column-oriented storage improves performance by storing columns together rather than rows.
This document provides an overview of data preprocessing techniques for data mining. It discusses data quality issues like accuracy, completeness, and consistency that require data cleaning. The major tasks of data preprocessing are described as data cleaning, integration, reduction, and transformation. Specific techniques for handling missing data, noisy data, and reducing redundancy during data integration are also summarized.
Data warehousing and online analytical processingVijayasankariS
The document discusses data warehousing and online analytical processing (OLAP). It defines a data warehouse as a subject-oriented, integrated, time-variant and non-volatile collection of data used to support management decision making. It describes key concepts such as data warehouse modeling using data cubes and dimensions, extraction, transformation and loading of data, and common OLAP operations. The document also provides examples of star schemas and how they are used to model data warehouses.
The document provides an introduction to databases and SQL. It defines what a database is as a collection of related data containing information relevant to an enterprise. It then discusses the properties of databases, what a database management system (DBMS) is, the typical functionality of a DBMS including defining, constructing, manipulating databases, and providing security. It also summarizes the components of a database system including fields, records, queries, and reports. The document then introduces SQL and its uses for data manipulation, definition, and administration. It provides examples of SQL statements for creating tables, inserting, querying, updating, and deleting data.
This document describes a simulator for database aggregation using metadata. The simulator sits between an end-user application and a database management system (DBMS) to intercept SQL queries and transform them to take advantage of available aggregates using metadata describing the data warehouse schema. The simulator provides performance gains by optimizing queries to use appropriate aggregate tables. It was found to improve performance over previous aggregate navigators by making fewer calls to system tables through the use of metadata mappings. Experimental results showed the simulator solved queries faster than alternative approaches by transforming queries to leverage aggregate tables.
The document discusses trends in data mining research, including mining complex data types like sequences, time series, graphs and networks. It covers various data mining methodologies like statistical data mining, visual data mining and views on the foundations of data mining. Statistical techniques discussed include regression, generalized linear models and discriminant analysis. Visual data mining involves using visualization to gain insights from large datasets and present data mining results.
1) The data dictionary is a virtual database that contains metadata (data about data) such as the definition of tables, fields, domains and other database objects. It provides information for data manipulation and processing.
2) Key data dictionary objects include domains, which define field attributes like type and length; data elements, which define field semantics; and tables, which store records of data. Transparent tables can be created to store custom tables.
3) System fields store system-related data like date and time, while structures store temporary data during runtime and views combine data from multiple tables.
Operational database systems are designed to support transaction processing while data warehouses are designed to support analytical processing and report generation. Operational systems focus on business processes, contain current data, and are optimized for fast updates. Data warehouses are subject-oriented, contain historical data that is rarely changed, and are optimized for fast data retrieval. The three main components of a data warehouse architecture are the database server, OLAP server, and client tools. Data is extracted from operational systems, transformed, cleansed, and loaded into fact and dimension tables in the data warehouse using the ETL process. Multidimensional schemas like star, snowflake, and constellation organize this data. Common OLAP operations performed on the data include roll-up,
Data warehousing interview_questionsandanswersSourav Singh
A data warehouse is a repository of integrated data from multiple sources that is organized for analysis. It contains historical data to support decision making. There are four fundamental stages of data warehousing: offline operational databases, offline data warehouse, real-time data warehouse, and integrated data warehouse. Dimensional modeling involves facts tables containing measurements and dimension tables containing context for the measurements. (191 words)
A data mart is a smaller subset of data from a data warehouse that is tailored to a specific business unit or function. It provides faster access to relevant data than searching an entire data warehouse. There are three main types of data marts - dependent, which get data from a data warehouse; independent, which access data directly from sources; and hybrid, which integrate multiple data sources. Data marts use either a star or snowflake schema to logically structure the data in dimension and fact tables for analysis. Implementing a data mart involves designing it, constructing the logical and physical structures, transferring data using ETL tools, configuring access, and ongoing management.
Data Bases, Data Warehousing, Data Mining, Decision Support System (DSS), OLAP, OLTP, MOLAP, ROLAP, Data Mart, Meta Data, ETL Process, Drill Up, Roll Down, Slicing, Dicing, Star Schema, SnowFlake Scheme, Dimentional Modelling
UNIT 2 DATA WAREHOUSING AND DATA MINING PRESENTATION.pptxshruthisweety4
The document discusses data warehousing and data warehouse architectures. It defines a data warehouse as a system that aggregates data from different sources into a consistent data store to support analysis and machine learning on huge volumes of historical data. It describes three common types of data warehouses and characteristics like being subject-oriented, integrated, and time-variant. It then outlines common data warehouse architectures including single tier, two tier, and three tier architectures and discusses components like the source layer, data staging, data warehouse layer, and analysis layer. Finally, it discusses properties of data warehouse architectures like separation of analytical and transactional processing and scalability.
The document discusses various concepts related to database design and data warehousing. It describes how DBMS minimize problems like data redundancy, isolation, and inconsistency through techniques like normalization, indexing, and using data dictionaries. It then discusses data warehousing concepts like the need for data warehouses, their key characteristics of being subject-oriented, integrated, and time-variant. Common data warehouse architectures and components like the ETL process, OLAP, and decision support systems are also summarized.
The document discusses the extraction, transformation, and loading (ETL) process used in data warehousing. It describes how ETL tools extract data from operational systems, transform the data through cleansing and formatting, and load it into the data warehouse. Metadata is generated during the ETL process to document the data flow and mappings. The roles of different types of metadata are also outlined. Common ETL tools and their strengths and limitations are reviewed.
The document discusses databases versus data warehousing. It notes that databases are for operational purposes like storage and retrieval for applications, while data warehouses are used for informational purposes like business reporting and analysis. A data warehouse contains integrated, subject-oriented data from multiple sources that is used to support management decisions.
This document provides an overview of ETL testing. It begins by explaining that an ETL tool extracts data from heterogeneous data sources, transforms the data, and loads it into a data warehouse. It then discusses the audience and prerequisites for ETL testing. Finally, it provides a copyright notice and table of contents for the document.
A Data Warehouse can be defined as a centralized, consistent data store or Decision Support System (OLAP), for the end business users for analysis, prediction and decision making in their business operations. Data from various enterprise-wide application/transactional source systems (OLTP), are extracted, cleansed, integrated, transformed and loaded in the Data Warehouse.
The document discusses data integration and the ETL process. It provides details on:
1. Data integration, which combines data from different sources to create a unified view, supporting business analysis. It involves extracting, transforming, and loading data.
2. The general approach of integration, which can be achieved through application, business process, and user interaction integration. Techniques include ETL, data federation, and data propagation.
3. Data integration for data warehousing, focusing on the "reconciled data layer" which harmonizes data from sources before loading into the warehouse. This involves transforming operational data characteristics.
UNIT - 1 Part 2: Data Warehousing and Data MiningNandakumar P
DBMS Schemas for Decision Support , Star Schema, Snowflake Schema, Fact Constellation Schema, Schema Definition, Data extraction, clean up and transformation tools.
This document provides an overview of SQL programming. It covers the history of SQL and SQL Server, SQL fundamentals including database design principles like normalization, and key SQL statements like SELECT, JOIN, UNION and stored procedures. It also discusses database objects, transactions, and SQL Server architecture concepts like connections. The document is intended as a training guide, walking through concepts and providing examples to explain SQL programming techniques.
It 302 computerized accounting (week 2) - sharifahalish sha
Here are some potential ways to represent relational databases other than using tables and relationships:
- Graph databases: Represent data as nodes, edges, and properties. Nodes represent entities, edges represent relationships between entities. Good for highly connected data.
- Document databases: Store data in flexible, JSON-like documents rather than rigid tables. Good for semi-structured or unstructured data.
- Multidimensional databases (OLAP cubes): Represent data in cubes with dimensions and measures. Good for analytical queries involving aggregation and slicing/dicing of data.
- Network/graph databases: Similar to graph databases but focus more on network properties like paths, connectivity etc. Good for social networks, recommendation systems.
-
The document discusses data warehouses and their characteristics. A data warehouse integrates data from multiple sources and transforms it into a multidimensional structure to support decision making. It has a complex architecture including source systems, a staging area, operational data stores, and the data warehouse. A data warehouse also has a complex lifecycle as business rules change and new data requirements emerge over time, requiring the architecture to evolve.
ETL is a process that extracts data from multiple sources, transforms it to fit operational needs, and loads it into a data warehouse or other destination system. It migrates, converts, and transforms data to make it accessible for business analysis. The ETL process extracts raw data, transforms it by cleaning, consolidating, and formatting the data, and loads the transformed data into the target data warehouse or data marts.
The document discusses interview questions and answers related to ETL (Extract, Transform, Load) processes. Some key points covered include:
- ETL involves extracting data from sources, transforming it, and loading it into a target database or data warehouse.
- The main steps in ETL are extracting data from sources, transforming it (which can involve multiple sub-steps), and loading it into the target.
- Initial loads populate tables for the first time by loading all records, while incremental loads apply dynamic changes over time on a schedule.
- Power Center is used for large data volumes while Power Mart handles smaller volumes. Partitioning can improve ETL performance by distributing transactions across multiple connections.
The document discusses process management in data warehousing. It describes the typical components involved - load manager, warehouse manager, and query manager. The load manager is responsible for extracting, transforming and loading data. The warehouse manager manages the data in the warehouse through indexing, aggregation and normalization. The query manager directs user queries to appropriate tables. Additionally, the document outlines the three perspectives for process modeling - conceptual, logical, and physical. The conceptual perspective represents interrelationships abstractly, the logical captures structure and data characteristics, while the physical provides execution details.
1. CROSS JOIN:
This join is a Cartesian join that does not necessitate any condition to join. The resultset
contains records that are multiplication of record number from both the tables.
How do you sort in sql:
order by" statement can be used to sort columns returned in a SELECT statement. The
ORDER BY clause is not valid in views, inline functions, derived tables, and subqueries,
unless TOP is also specified.
How do you select unique records in SQL?:
Using the “DISTINCT” clause
select distinct FirstName from Employees
Union & Union All:
The difference between Union and Union all is that Union all will not eliminate duplicate
rows, instead it just pulls all rows from all tables fitting your query specifics and
combines them into a table.
If you know that all the records returned are unique from your union, use UNION ALL
instead, it gives faster results
Truncate & Delete:
Truncate an Delete both are used to delete data from the table. These both command will
only delete data of the specified table, they cannot remove the whole table data
structure.Both statements delete the data from the table not the structure of the table.
TRUNCATE is a DDL (data definition language) command whereas DELETE is a DML
(data manipulation language) command.
You can use WHERE clause(conditions) with DELETE but you can't use WHERE clause
with TRUNCATE .
You cann't rollback data in TRUNCATE but in DELETE you can rollback
data.TRUNCATE removes(delete) the record permanently.
A trigger doesn’t get fired in case of TRUNCATE whereas Triggers get fired in DELETE
command.
Compute Clause in SQL?:
2. Generates totals that appear as additional summary columns at the end of the result set.
When used with BY, the COMPUTE clause generates control-breaks and subtotals in the
result set.
USE Database;
GO
SELECT CustomerID, OrderDate, SubTotal, TotalDue
FROM Sales.SalesOrderHeader
WHERE ID = 1
ORDER BY OrderDate
COMPUTE SUM(SubTotal), SUM(TotalDue);
What is Datawarehousing:
In computing, a data warehouse (DW) is a database used for reporting and analysis. The
data stored in the warehouse is uploaded from the operational systems. The data may pass
through an operational data store for additional operations before it is used in the DW for
reporting.
A data warehouse maintains its functions in three layers: staging, integration, and access.
Staging is used to store raw data for use by developers. The integration layer is used to
integrate data and to have a level of abstraction from users. The access layer is for getting
data out for users.
The term Data Warehouse was coined by Bill Inmon in 1990, which he defined in the
following way: "A warehouse is a subject-oriented, integrated, time-variant and non-
volatile collection of data in support of management's decision making process". He
defined the terms in the sentence as follows:
Subject Oriented:
Data that gives information about a particular subject instead of about a company's
ongoing operations.
Integrated:
Data that is gathered into the data warehouse from a variety of sources and merged into a
coherent whole.
Time-variant:
All data in the data warehouse is identified with a particular time period.
Non-volatile
Data is stable in a data warehouse. More data is added but data is never removed. This
enables management to gain a consistent picture of the business.
What is Data Mart: A data mart is the access layer of the data warehouse environment
3. that is used to get data out to the users. The data mart is a subset of the data warehouse
which is usually oriented to a specific business line or team.
A data mart is a simple form of a data warehouse that is focused on a single subject (or
functional area), such as Sales, Finance, or Marketing. Data marts are often built and
controlled by a single department within an organization. Given their single-subject
focus, data marts usually draw data from only a few sources. The sources could be
internal operational systems, a central data warehouse, or external data.
A data mart is a repository of data gathered from operational data and other sources that
is designed to serve a particular community of knowledge workers. In scope, the data
may derive from an enterprise-wide database or data warehouse or be more specialized.
The emphasis of a data mart is on meeting the specific demands of a particular group of
knowledge users in terms of analysis, content, presentation,and ease-of-use. Users of a
data mart can expect to have data presented in terms that are familiar.
What are Fact Table & Dimension Tables:
In data warehousing, a fact table consists of the measurements, metrics or facts of a
business process. It is often located at the centre of a star schema or a snowflake schema,
surrounded by dimension tables.
Fact tables provide the (usually) additive values that act as independent variables by
which dimensional attributes are analyzed. Fact tables are often defined by their grain.
The grain of a fact table represents the most atomic level by which the facts may be
defined. The grain of a SALES fact table might be stated as "Sales volume by Day by
Product by Store". Each record in this fact table is therefore uniquely defined by a day,
product and store. Other dimensions might be members of this fact table (such as
location/region) but these add nothing to the uniqueness of the fact records. These
"affiliate dimensions" allow for additional slices of the independent facts but generally
provide insights at a higher level of aggregation (a region contains many stores).
In data warehousing, a dimension table is one of the set of companion tables to a fact
table.
The fact table contains business facts or measures and foreign keys which refer to
candidate keys (normally primary keys) in the dimension tables.
Contrary to fact tables, the dimension tables contain descriptive attributes (or fields)
which are typically textual fields or discrete numbers that behave like text. These
attributes are designed to serve two critical purposes: query constraining/filtering and
query result set labeling.
Dimension attributes are supposed to be:
Verbose - labels consisting of full words,
Descriptive,
Complete - no missing values,
Discretely valued - only one value per row in dimensional table,
Quality assured - no misspelling, no impossible values.
4. Snake flow schema to Design the tables:
In computing, a snowflake schema is a logical arrangement of tables in a
multidimensional database such that the entity relationship diagram resembles a
snowflake in shape. The snowflake schema is represented by centralized fact tables which
are connected to multiple dimensions.
The snowflake schema is similar to the star schema. However, in the snowflake schema,
dimensions are normalized into multiple related tables, whereas the star schema's
dimensions are normalized with each dimension represented by a single table. A complex
snowflake shape emerges when the dimensions of a snowflake schema are elaborate,
having multiple levels of relationships, and the child tables have multiple parent tables
("forks in the road"). The "snowflaking" effect only affects the dimension tables and NOT
the fact tables.
Processing of ETL Dataware housing:
Extract, transform and load (ETL) is a process in database usage and especially in data
warehousing that involves:
Extracting data from outside sources
Transforming it to fit operational needs (which can include quality levels)
Loading it into the end target (database or data warehouse)
Extract - The first part of an ETL process involves extracting the data from the source
systems. In many cases this is the most challenging aspect of ETL, as extracting data
correctly will set the stage for how subsequent processes will go.
Transform - The transform stage applies a series of rules or functions to the extracted data
from the source to derive the data for loading into the end target.
Load - The load phase loads the data into the end target, usually the data warehouse
(DW). Depending on the requirements of the organization, this process varies widely
What is BCP:
The bcp utility copies data between an instance of SQL Server and a data file in a user-
specified format.The Bulk Copy Program (BCP) is a command-line utility that ships with
Microsoft SQL Server. With BCP, you can import and export large amounts of data in and
out of SQL Server databases quickly and easily.
DTS in SQL Server:
Data Transformation Services, or DTS, is a set of objects and utilities to allow the
automation of extract, transform and load operations to or from a database. The objects
are DTS packages and their components, and the utilities are called DTS tools. DTS was
5. included with earlier versions of Microsoft SQL Server, and was almost always used with
SQL Server databases, although it could be used independently with other databases.
DTS allows data to be transformed and loaded from heterogeneous sources using OLE
DB, ODBC, or text-only files, into any supported database. DTS can also allow
automation of data import or transformation on a scheduled basis, and can perform
additional functions such as FTPing files and executing external programs. In addition,
DTS provides an alternative method of version control and backup for packages when
used in conjunction with a version control system, such as Microsoft Visual SourceSafe .
Multi dimensional Analysis:
Multidimensional analysis is a data analysis process that groups data into two or more
categories: data dimensions and measurements. For example, a data set consisting of the
number of wins for a single football team at each of several years is a single-dimensional
(in this case, longitudinal) data set. A data set consisting of the number of wins for several
football teams in a single year is also a single-dimensional (in this case, cross-sectional)
data set. A data set consisting of the number of wins for several football teams over
several years is a two-dimensional data set.
In many disciplines, two-dimensional data sets are also called panel data. While, strictly
speaking, two- and higher- dimensional data sets are "multi-dimensional," the term
"multidimensional" tends to be applied only to data sets with three or more dimensions.
For example, some forecast data sets provide forecasts for multiple target periods,
conducted by multiple forecasters, and made at multiple horizons. The three dimensions
provide more information than can be gleaned from two dimensional panel data sets.
Bulk Insert:
The Bulk Insert task provides an efficient way to copy large amounts of data into a SQL
Server table or view. For example, suppose your company stores its million-row product
list on a mainframe system, but the company's e-commerce system uses SQL Server to
populate Web pages. You must update the SQL Server product table nightly with the
master product list from the mainframe. To update the table, you save the product list in a
tab-delimited format and use the Bulk Insert task to copy the data directly into the SQL
Server table.
There are primary three ways in which we store information in OLAP:-
MOLAP
Multidimensional OLAP (MOLAP) stores dimension and fact data in a persistent data
store using compressed indexes. Aggregates are stored to facilitate fast data access.
MOLAP query engines are usually proprietary and optimized for the storage format used
by the MOLAP data store. MOLAP offers faster query processing than ROLAP and
usually requires less storage. However, it doesn’t scale as well and requires a separate
6. database for storage.
ROLAP
Relational OLAP (ROLAP) stores aggregates in relational database tables. ROLAP use
of the relational databases allows it to take advantage of existing database resources,
plus it allows ROLAP applications to scale well. However, ROLAP’s use of tables to
store aggregates usually requires more disk storage than MOLAP, and it is generally not
as fast.
HOLAP
As its name suggests, hybrid OLAP (HOLAP) is a cross between MOLAP and ROLAP.
Like ROLAP, HOLAP leaves the primary data stored in the source database. Like
MOLAP,
HOLAP stores aggregates in a persistent data store that’s separate from the primary
relational database. This mix allows HOLAP to offer the advantages of both MOLAP
and ROLAP. However, unlike MOLAP and ROLAP, which follow well-defined
standards,
HOLAP has no uniform implementation.