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Training Module:


TimeXtender can help you build a data estate, understand its various use cases, and see common reference architectures.

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goodbye, data management

HELLO, Data Empowerment

why do data teams need timextender?

Data is the lifeblood of modern businesses.

Data-empowered organizations are:

However, data teams are constantly struggling.

The results are in.

The "Modern Data Stack" is NOT meeting the needs of modern businesses.

To become data-empowered

organizations require fast access to reliable data for multiple use cases.

To accomplish this, data must be extracted from various source systems, cleaned, consolidated, and curated into reliable data sets for business consumption. Large organizations have many use cases for data, requiring a 3-tier architecture...


Data is ingested and stored in its raw form to be used by data scientists for AI and machine learning as well as feed the subsequent layers.


Data is cleaned and consolidated into a single version of truth to deliver reliable insights to business analysts.


Data is curated into a subset of models relevant for business users from different domains (sales, marketing, finance, etc.).

We refer to this as the DATA ESTATE

Surprisingly, these architectures are still commonly built with large teams of experts, using stacks of tools, manual hand-coding, and handwritten documentation over several months or even years.

No wonder 85% of these projects fail.

Ask yourself...

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If my company needed a web page quickly, would I code it by hand using HTML and CSS?

Or would I use a modern drag-and-drop website builder?

If my colleagues requested an interactive chart or graph ASAP? Would I code it by hand using python or C#?

Or would you use Excel, Power BI, or Tableau?

So, if data is so important and time-sensitive, why would you hand-code your data estate?

Look, you can build your data estate like it's 1999... But you don't have to.

lineage & documentation

Instantly generate end-to-end data lineage and documentation to ensure users their data is complete, accurate, and reliable.

Agile, Low-Code

Automatically generate data cleansing and transformation code, allowing for faster, less error-prone development.

intelligent data pipelines

Since all object dependencies are fully managed, multiple pipelines are run in parallel and intelligently optimized by learning from past executions


With solutions stored as metadata, best-practice code is

being generated specifically for the platform you choose. Allowing you to switch to new modern cloud platforms, without needing to rebuild.

Timextender instances

TimeXtender solutions are built with the components of the data estate in mind. However, we refer to them slightly differently.





The Modern Data Warehouse (MDW) is

where you cleanse, transform, and consolidate data into a "single version of truth".


The Shared Semantic Layer (SSL) is

where you combine relevant data into models or data marts, and publish to various endpoints,

such as dashboards and data visualization tools.

when is timextender used?


Build new data infrastructure

TimeXtender is the fastest way to build a new Data Lake, Data Warehouse, or fully fledged Data Estate

integrate data for

mergers and acquisitions

Due to its speed and agility, many customers pick TimeXtender to quickly integrate data as they acquire new companies

finally achieve

self-service analytics

Finally keep up with business demands for data through an agile low-code approach that will satisfy even the most data hungry users

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Modernize existing data infrastructure

TimeXtender integrates with the latest cloud analytics services, so you can modernize without re-skilling.

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prepare data

for AI/ML

Extract and cleanse data 10x faster that traditional methods, you can prep data for your AI models in less time.


governed models for BI

Build dashboards and reports faster by maintaining and well organized data infrastructure.

who uses timextender?

1,000s of companies in nearly 100 countries around the world use TimeXtender.

Every industry vertical needs data, so any industry vertical can use TimeXtender.


"Using TimeXtender’s native capabilities to deploy to any supported Azure Data Service, the UAT environment was successfully migrated to SQL DB MI in just 2 days"


The world-famous jewelry company from Denmark, PANDORA, selected TimeXtender to access and analyze the increasingly large volumes of company data to maintain its rapid growth.

michigan's capital

"Using the TimeXtender platform to model, cleanse, and create a consistent language between systems has made accessing and utilizing data much faster.”


"Everyone now has access to the same standardized and validated data, meaning we now all have a single source of truth.”

how is timextender deployed?

TimeXtender can be deployed using many, different target platforms and deployment scenarios. Below are some of the most common and recommended reference architectures.

On-Premises SQL Server

Build a fully On-Premise Data Warehouse using TimeXtender

Azure SQL

Cost-effective cloud solution for small to medium size data warehouses.

Ideal for production data warehouses smaller than 1 TB


Highly scale-able solution for enterprise-grade analytics.

Ideal for production data warehouses larger than 1 TB


Ideal for needs where in high performance, simplicity, concurrency and affordability are ​pillars for the data warehouse design.


Ideal Solution for managed, scalable, and highly available relational database service in the cloud with simplified administration and robust security

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Section Quiz...

Planning to take the Solution Architect exam? Then you want to be sure these questions are easy to answer.

What are the 3 components of a Data Estate?

What are 3 common use cases for TimeXtender?

What are 3 common reference architectures, and how do they differ?

When you're ready, see Answers Below

Section Quiz Answers

What are the 3 components of a Data Estate?

Data Lake, Data Warehouse, Data Marts

What are 3 common use cases for TimeXtender?

Build new data Infrastructure,

Modernize an existing data warehouse,

Prepare data for AI/ML

What are 3 common reference architectures, and how do they differ?

On-Premises SQL Server, Azure SQL, Azure Synapse

Congratulations! You've completed the training module



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