Dale Anderson

Dale Anderson is a Customer Success Architect at Talend. Over a 30 year career, Mr. Anderson has gained extensive experience in a range of disciplines including systems architecture, software development, quality assurance, and product management and honed his skills in database design, modeling, and implementation, as well as data warehousing and business intelligence. A keen advocate for an appropriate temperance of the software workflow process, he has applied his innovations to the often overlooked lifecycle of a database. The Database Development Lifecycle Mr. Anderson has developed incorporates data modeling best practices to address the complexities involved in modeling adaptations, multi-environment fulfilments, fresh installations, schema upgrades, and data migrations for any database implementation.

Building a Governed Data Lake in the Cloud

The main purpose of a Data Lake is to provide full and direct access to raw (unfiltered) organizational data as an alternative to storing varying and sometimes limited datasets in scattered, disparate data silos.

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Data Model Design & Best Practices – Part 2

| July 31, 2017 | Big Data Integration Database Integration Developer Step-by-Step ETL / ELT

  What is a Data Model?  As Talend developers, we see them every day, and we think we know what they are: A structural definition of a business system data  A graphical representation of business data  A data foundation upon which to build business solutions  These may …

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Data Model Design and Best Practices – Part 1

The Data Model is the backbone of almost all of our high value, mission critical, business solutions from e-Commerce and Point-of-Sale, through Financial, Product, and Customer Management, to Business Intelligence and IoT. Without a proper Data Model, where is the business data? Probably: Lost!

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Data Model Design and Best Practices – Part 1

| May 5, 2017 | Database Integration Developer Step-by-Step ETL / ELT

Business Applications, Data Integration, Master Data Management, Data Warehousing, Big Data, Data Lakes, and Machine Learning; these all have (or should have) a common and essential ingredient: A Data Model; let us NOT forget about that; or, as in many situations I run into, ignore it completely! The …

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