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Information Storage Facility Datawarehouse4u.information Business-intelligence Tools – Decision Support and Date Warehouse Jingyi Lu. Diagram of Decision Support System OLAP vs OLTP What is a Data Warehouse? Extract dimensional modeling, transform,

Presentation on theme: “Decision Support and Date Warehousing Jingyi Lu. Outline Decision Support System OLAP vs. OLTP What is a Data Warehousing? Extract, Transform Dimensional Modeling, “— Presentation transcript:

Information Storage Facility Datawarehouse4u.information Business-intelligence Tools

2 Outline Decision Support System OLAP vs OLTP What is a Date Warehouse? Extract, transform and load dimensional modeling (ETL)

Sumber Data Dan Manajemen Basis Data

3 Decision Support System Information technology to help the knowledge worker (executive, manager, analyst) make faster and better decisions. – What are the sales volumes by region and product category for last year? – Which orders should we fill to maximize revenue? – Will a 10% discount increase sales volume enough?

4 Decision Support Systems Designed to facilitate the decision-making process So much information that it is difficult to extract it all from a traditional database Need for a more comprehensive data storage facility  Data Warehouse

5 Decision Support Systems Extract information from data to use as a basis for decision-making Used at all levels of the organization Tailored to specific business areas Ad Hoc requests to extract and display information Combines data on past operations with business activities

7 OLAP vs OLTP OLTP (Online Transaction Processing): characterized by a large number of short online transactions.—–> Operational Database OLAP (Online Analytical Processing): characterized by a relatively small volume of transactions. Queries are often very complex and involve aggregations.——> Data Warehouse

Understanding Business Intelligence With Data Warehousing

9 What is a Data Warehouse The repository for DSS is DATA WAREHOUSE Definition: An integrated, subject-oriented, time-varying, non-volatile database that provides decision support.

10 Integrated A data warehouse is a centralized, consolidated database that integrates data drawn from across the organization  Multiple sources  Diverse sources  Diverse formats

11 Subject-oriented data is organized and optimized to answer questions from different functional areas Data is organized and summarized by topics  Sales / Marketing / Finance / Distribution / Etc.

12 Time Variant Data warehouse represents the flow of data over time May contain forecasted data from statistical models Data is uploaded periodically, then time-dependent data is recalculated

Data Warehouse References

13 Non-volatile Once the data is entered, it is NEVER removed Represents the entire history of the company  Short-term history is continuously added to it  Always growing  Must support terabyte databases and multiprocessors Read-only database for data analysis and query processing

14 Dimensional Modeling Dimension  a dimension is a data element that categorizes each element in a data set into non-overlapping regions Facts  a value or measurement that represents a fact about the managed object or system.  usually numerical values ​​that can be aggregated

15 Dimension Modeling Database is a set of facts (points) in a multidimensional space Fact Tables  contains business facts or measures and foreign keys that refer to the primary keys in the dimension tables Dimension Tables  Each dimension table has a set from attributes, e.g. Day , Month , Year of Date  Dimension attributes can be related by a partial order hierarchy: e.g. Day > Month > Year

19 Load Extract Transformation – ETL To extract data from the source and load it into the data warehouse – a simple process of copying data from one database to another Data is extracted from an OLTP database, transformed to match the data warehouse schema and are loaded into the data warehouse database Many data warehouses also include data from non-OLTP systems such as text files, legacy systems, and spreadsheets; such data also requires extraction, transformation, and loading When defining ETL for a data warehouse, it is important to think of ETL as a process rather than a physical implementation

Online Transaction Processing

20 ETL ETL is often a complex combination of process and technology that consumes a significant portion of the data warehouse development effort and requires the skills of business analysts, database designers, and application developers It is not a one-time event as new data is add to Data Warehouse periodically – monthly, daily, hourly Because ETL is an integral, ongoing and recurring part of a data warehouse  Automated  Well documented  Easily changeable

21 ETL Staging Database ETL operations should be performed on a relational database server separate from the source databases and the data warehouse database Creates a logical and physical separation between the source systems and the data warehouse Minimizes the impact of intensive periodic ETL activity on the source and databases data warehouses

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To make this website work, we register user data and share it with processors. To use this website, you must agree to our Privacy Policy, including our Cookie Policy. For the sake of readers who are hearing the term “Data Warehouse” for the first time, let me explain briefly. A data warehouse is an integrated, non-volatile, topic-oriented and time-varying data store for uncovering trends, patterns and correlations that provide valuable business information and insight. The focus of this article is to show you the importance of cloud data storage in a data-driven economy. We’ll take a quick look at different data storage concepts and evaluate two scenarios—structured and unstructured data—to design our first data warehouse.

Pdf) Concepts And Fundaments Of Data Warehousing And Olap

With exponentially greater volume and complexity of data, with data distributed across different databases, applications, domains or across different sources such as websites, mobile phones and in different structured or unstructured formats. As a data-driven company, monitoring end-user behavior and measuring customer adoption means turning that raw data into actionable information by cleaning, transforming and integrating the data to make it a single source of truth for the organization . This purified, transformed and integrated data needs a unique, cost-effective single storage location, and that location is the Data Warehouse.

Second, to perform any trend analysis, we need to examine data between different time combinations of year, quarter, month, week, day, weekday-weekend. In this case, to facilitate decision-making using our historical data, the most appropriate option is to design a data warehouse.

Finally, machine-generated data typically have a poor signal-to-noise ratio. It can contain valuable data, but also a lot of “noise”. This means we have to store everything to find the useful parts. Is it possible to store this data cost-effectively and consume it in a meaningful way through analytics? This makes the cloud—with its near-infinite scalability, external storage capability, and seamless integration—an ideal place to host data storage.

Business analysts, data analysts, data scientists, data engineers, managers, as well as senior executives benefit from data warehousing through self-service business intelligence (BI). It’s everyone’s dream in an organization — all the information about the organization’s activities gathered in one place, one source of truth, available at a glance on the dashboard, with just a click of a button. It may seem like a distant dream, but how do we turn this dream into reality? First, we need to plan our data storage system. Modeling your data warehouse is the first step in this direction.

Pdf) Olap Systems

SalesLoft’s core platform has thousands of users on various surfaces. Due to the lack of integrated data, account managers, SDRs, CSMs, ISMs cannot have a 360-degree view of their customers while making important decisions. They would also like to examine historical data to identify interesting and valuable relationships. Currently, this is a manual, time-consuming process and does not allow understanding historical trends. So we are asked to design a system that can help them make decisions quickly and provide return on investment (ROI). To start designing the data warehouse, we need to follow several steps.

The properties of cloud technology – especially scalability, cost-effectiveness, unlimited resources, natural integration points – make it particularly suitable for data storage. With an exponentially growing volume of data (user activity and machine-generated data), we chose Amazon Redshift to build our data warehouse. Redshift provides the benefits of columnar storage, Redshift Spectrum to separate expensive computation from storage and ingest and query both semi-structured and structured data formats.

Evaluating an ETL tool is quite a challenge. With a wide variety of options on the market, it’s hard to know where to start. We selected three different ETL tools based on their capabilities to perform full load, incremental load with change capture (CDC), process chains, job scheduling and monitoring, job success/failure notification to name a few. -important criteria. With a very intuitive development interface, Matillion ETL for Redshift came out the winner.

Last but not least, we had a pre-existing web-based BI tool ‘Looker’ for connecting to the data warehouse, running queries, building charts and dashboards. We found Looker easy to use for both data analysts and business users.

Data Were House Notes

From there, if done right, we knew our data warehouse would be up and running in less than 3 months. Therefore, most of the time from here on was spent gathering requirements, building the ETL pipeline and configuring the “Looker” to extract information from the data warehouse.

The data warehouse project phases listed below are similar to the

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