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The data warehouse ETL toolkit: practical techniques for extracting, cleaning, conforming, and delivering data / Ralph Kimball, Joe Caserta. The Data Warehouse Toolkit: The Definitive Guide to Dimensional data warehouse and business intelligence industry's thought leader on the. /keybase/public/ascensao/Kimball & Caserta -The Data Warehouse ETL Toolkit [ Wiley ].pdf. Download Raw. This file was signed by: ascensao.

Data Warehouse Etl Toolkit Pdf

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The Data Warehouse. ETL Toolkit. Practical Techniques for. Extracting, Cleaning, . Conforming, and. Delivering Data. Ralph Kimball. Joe Caserta. WILEY. 07/06/ Wiley: The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data Ralph Kimball, Joe Cas. The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Kimball's Data Warehouse Toolkit Classics, 3 Volume Set.


And, of course, the ETL team often discovers additional capabilities in the data sources that expand end users decision-making capabilities. The lesson here is that even during the most technical back-room development steps of building the ETL system, a dialog amongst the ETL team, the data warehouse architects, and the end users should be maintained.

In a larger sense, business needs and the content of data sources are both moving targets that constantly need to be re-examined and discussed.

Compliance Requirements In recent years, especially with the passage of the Sarbanes-Oxley Act of , organizations have been forced to seriously tighten up what they report and provide proof that the reported numbers are accurate, complete, and have not been tampered with.

Of course, data warehouses in regulated businesses like telecommunications have complied with regulatory reporting requirements for many years. But certainly the whole tenor of financial reporting has become much more serious for everyone.

Several of the financial-reporting issues will be outside the scope of the data warehouse, but many others will land squarely on the data warehouse.

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Typical due diligence requirements for the data warehouse include: Archived copies of data sources and subsequent stagings of data Proof of the complete transaction flow that changed any data Fully documented algorithms for allocations and adjustments Proof of security of the data copies over time, both on-line and off-line Data Profiling As Jack Olson explains so clearly in his book Data Quality: The Accuracy Dimension, data profiling is a necessary precursor to designing any kind of system to use that data.

A good dataprofiling [system] can process very large amounts of data, and with theskills of the analyst, uncover all sorts of issues that need to be addressed.

For example, Jack points out that a data source that perfectly suits the needs of the production system, such as an order-taking system, may be a disaster for the data warehouse, because the ancillary fields the data warehouse hoped to use were not central to the success of the order-taking process and were revealed to be unreliable and too incomplete for data warehouse analysis.

Data profiling is a systematic examination of the quality, scope, and context of a data source to allow an ETL system to be built. At one extreme, a very clean data source that has been well maintained before it arrives at the data warehouse requires minimal transformation and human intervention to load directly into final dimension tables and fact tables.

The profiling step not only gives the ETL team guidance as to how much data cleaning machinery to invoke but protects the ETL team from missing major milestones in the project because of the unexpected diversion to build a system to deal with dirty data.

Do the data profiling up front! Use the data-profiling results to prepare the business sponsors for the realistic development schedules, the limitations in the source data, and the need to invest in better data-capture practices in the source systems.

Security Requirements The general level of security awareness has improved significantly in the last few years across all IT areas, but security remains an afterthought and an unwelcome additional burden to most data warehouse teams. The basic rhythms of the data warehouse are at odds with the security mentality. The data warehouse seeks to publish data widely to decision makers, whereas the security interests assume that data should be restricted to those with a need to know.

Throughout the Toolkit series of books we have recommended a role based approach to security where the ability to access the results from a data warehouse is controlled at the final applications delivery point. This means that security for end users is not controlled with grants and revokes to individual users at the physical table level but is controlled through roles defined and enforced on an LDAP-based network resource called a directory server.

It is then incumbent on the end users applications to sort out what the authenticated role of a requesting end user is and whether that role permits the end user to view the particular screen being requested.

Etl Toolkit by Ralph Kimball PDF

This view of security is spelled out in detail in Data Warehouse LifecycleToolkit. The good news about the role-based enforcement of security is that the ETL team should not be directly concerned with designing or managing end user security.

A large percentage, if not the majority, of malicious attacks on IT infrastructure comes from individuals who have legitimate physical access to company facilities. Additionally, security must be extended to physical backups. If a tape or disk pack can easily be removed from the backup vault, security has been compromised as effectively as if the on-line passwords were compromised.

Data Integration Data integration is a huge topic for IT because ultimately IT aims to make all systems work together seamlessly. The degree view of the business is the business name for data integration. In many cases, serious data integration must take place among the primary transaction systems of the organization before any of that data arrives at the data warehouse.

But rarely is that data integration complete, unless the organization has settled on a single enterprise resource planning ERP system, and even then it is likely that other important transaction-processing systems exist outside the main ERP system. In this section, data integration takes the form of conforming dimensions and conforming facts.

Requirements - Data Warehouse ETL Toolkit

Conforming dimensions means establishing common dimensional attributes often textual labels and standard units of measurement across separate databases so that drill across reports can be generated using these attributes. Conforming facts means agreeing on common business metrics such as key performance indicators KPIs across separate databases so that these numbers can be compared mathematically by calculating differences and ratios.

In the ETL system, data integration is a separate step identified in our data flow thread as the conform step.

Physically, this step involves enforcing common names of conformed dimension attributes and facts, as well as enforcing common domain contents and common units of measurement. Data Latency The data latency requirement describes how quickly the data must be delivered to end users.

The Data Warehouse ETL Toolkit

Cowritten by Ralph Kimball, the worlds leading data warehousing authority, whose previous books have sold more than , copies Delivers real-world. Cowritten by Ralph Kimball, the worlds leading data warehousing authority. Even with that flaw, the ETL Toolkit turn out as an outstanding reference to state.

ETL load, or the process economist december 18th pdf of moving data from a source system such as.

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Ralph Kimball, Margy Ross, Warren. It is totally understandable why Googles search result dont include ETL or.Cowritten by Ralph Kimball, the worlds leading datawarehousing authority, whose previous books have sold. And what more, learnt a great deal more about Data warehousing : I recommend it to anyone who has even the slightest of inclination towards databases, data modelling and data analysis.

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Ralph Kimball is known worldwide as an innovator, writer, educator, speaker. Kindle Edition Verified Purchase. The bias toward driving the data to the front room for presentation forces data quality issues to the surface where they must be dealt with and the loop to operational systems or perhaps even flawed ETL transforms!

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