Business Intelligence Record Tool – Business intelligence is the process by which companies use strategies and techniques to analyze current and historical data for the purpose of improving strategic decision-making and providing competitive advantage.
Business intelligence systems combine data collection, data storage, and knowledge management with data analytics to evaluate and transform complex data into meaningful, actionable information that can be used to support more effective strategic, tactical, and operational insights and decision-making. A business intelligence environment consists of a variety of technologies, applications, processes, strategies, products, and technology architectures used to enable the collection, analysis, presentation, and dissemination of internal and external business information.
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Business intelligence technologies use advanced statistical and predictive analytics to help businesses draw conclusions from data analysis, discover patterns, and predict future events in business operations. Business intelligence reporting is not a linear practice, but a continuous, multifaceted cycle of data access, exploration and information sharing. Common business intelligence capabilities include:
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Modern business intelligence systems prioritize self-service analytics, enabling companies to gain market insights and improve performance through comprehensive data discovery tools, methods, processes, and platforms. These business intelligence solutions include:
Business intelligence platforms allow companies to build custom business intelligence applications that leverage existing data architectures and provide information that analysts can query and visualize. Modern business intelligence platforms support self-service analytics, making it easy for end users to create their own dashboards and reports.
A simple user interface combined with flexible business intelligence backend software allows users to connect to a variety of data sources including NoSQL databases, Hadoop systems, cloud platforms and traditional data warehouses to develop consistent views of diverse data.
As artificial intelligence and machine learning continue to grow and businesses strive to become more data-driven and collaborative, business intelligence continues to evolve, allowing users to incorporate AI insights and harness the power of data visualization. Popular business intelligence platform providers include Oracle, Microsoft, IBM and Salesforce.
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The importance of business intelligence continues to grow as companies face the challenge of gaining insights from ever-increasing streams of raw data and massive amounts of information (big data). By using business intelligence systems, companies can gain a comprehensive view of an organization’s data and transform it into insights into business processes to make improved and strategic business decisions.
Business intelligence helps organizations analyze data in historical context, optimize operations, track performance, accelerate and improve decision-making, identify and eliminate business problems and inefficiencies, identify market trends and patterns, We help you generate new revenue and profitability, increase productivity and accelerate growth. Analyze customer behavior, compare data to competitors and ultimately gain a competitive advantage over them.
Business intelligence and data science both provide methods for interpreting data with the goal of supporting improved tactical decision-making. The main difference lies in the type of question. Whereas business intelligence interprets historical data and provides new value from currently known information, data science focuses more on predictive analytics. Simply put, business intelligence asks, “What happened and what needs to change?” And data science asks, “Why did X happen and what would happen if we did Z?”
Data science can be viewed as an evolution of business intelligence in response to the growing volume and complexity of data and data entry technologies. If business intelligence is designed to manage static, highly structured data and provide solutions for today’s decisions, data science systems are designed to manage high-speed, multi-structured data and continuously improve algorithms to provide future solutions. It has been.
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Data science powers business intelligence, providing algorithmic models that business intelligence developers can feed into prepared data. Business intelligence analysts provide expertise in business intelligence analysis needs. The two disciplines can work together to build powerful models for predicting the future.
It redefines the limits of speed and scale in big data analytics, providing a versatile data science platform that can dramatically accelerate legacy business intelligence, data visualization and GIS tools as well as custom analytics applications. .iDB can accelerate various data visualization and business intelligence tools by executing queries much faster than existing mainstream analytics systems. This example scenario shows how to ingest data from an on-premises data warehouse into a cloud environment. The service is then delivered using a business intelligence (BI) model. This approach can be the end goal or the first step towards full modernization with cloud-based components.
The following steps are based on an Azure Synapse Analytics end-to-end scenario. Use Azure Pipelines to ingest data from SQL databases into Azure Synapse SQL pools, then transform the data for analysis.
An organization has a large on-premises data warehouse stored in a SQL database. Organizations want to use Azure Synapse to perform analytics and then use Power BI to deliver those insights.
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Azure AD authenticates users connecting to Power BI dashboards and apps. Single sign-on is used to connect to data sources in Azure Synapse provisioned pools. Authentication occurs at the source.
When running an automated extract-transform-load (ETL) or extract-load-transform (ELT) process, it is most efficient to load only data that has changed since the previous run. This is called incremental load as opposed to full load which loads all data. Incremental loading requires a way to identify changed data. The most common approach is
A value that tracks the latest value of some column in the source table, such as a date/time column or a unique integer column.
Starting in SQL Server 2016, you can use temporal tables, which are system-versioned tables that keep a complete history of data changes. The database engine automatically records all change records in a separate history table. You can query historical data by adding
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Clause in the query. Internally, the database engine queries the history table, but it is transparent to the application.
Change data capture (CDC) is available in earlier versions of SQL Server. This approach is less convenient than temp tables because you have to query a separate change table and changes are tracked by log sequence number rather than timestamp.
Temporary tables are useful for dimensional data that may change over time. Fact tables typically represent immutable transactions, such as sales, in which case it makes no sense to keep system version history. Instead, transactions usually have a column representing the date of the transaction that can be used as a watermark value. For example, in AdventureWorks Data Warehouse
This scenario uses the AdventureWorks sample database as a data source. The incremental data load pattern is implemented to load only data that has been modified or added since the most recent pipeline execution.
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A built-in metadata-driven copy tool within Azure Pipelines incrementally loads all tables contained in a relational database. You can navigate through a wizard-based environment to connect the data copy tool to your source database and configure incremental or full load for each table. The data copy tool then generates both a pipeline and SQL scripts to create the necessary control tables to store the data for the incremental load process (eg high watermark values/columns for each table). When these scripts run, the pipeline is ready to load all the tables from the source data warehouse into the Synapse dedicated pool.
The tool creates three pipelines that loop through all tables in the database before loading the data.
A copy activity copies data from a SQL database to an Azure Synapse SQL pool. In this example, since the SQL database is in Azure, the Azure integration runtime is used to read data from the SQL database and write data to the specified staging environment.
Then use a copy statement to load the data from the staging environment into the Synapse dedicated pool.
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Pipelines in Azure Synapse are used to define an ordered set of operations to complete an incremental load pattern. Triggers are used to start pipelines that can be triggered either manually or at a specified time.
Because the sample database for the reference architecture is not large, we created replicated tables without partitions. For production workloads, using distributed tables can improve query performance. See Guidelines for Designing Distributed Tables in Azure Synapse. The example script uses a static resource class to run a query.
In a production environment, consider creating staging tables with a round-robin distribution. Then transform and move the data to a production table with a clustered columnstore index that has the best overall query performance. Columnstore indexes are optimized for queries that retrieve many records. Columnstore indexes do not perform well for singleton lookups, i.e. single row lookups. If you need to do frequent singleton lookups, you can add a nonclustered index to the table. Singleton lookups can run much faster using nonclustered indexes. However, singleton lookups are generally less common in data warehouse scenarios than in OLTP workloads. For more information, see Table indexing in Azure Synapse.
Data type. In this case, consider a heap or clustered index. You can put that column in a separate table.
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Power BI Premium supports several options for connecting to data sources in Azure, specifically to Azure Synapse provisioned pools.
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