What is Timextender¶
How Timextender helps you build a data estate, what its various use cases are, and what the common reference architectures look like.
Goodbye, data management. Hello, data empowerment.
Why do data teams need Timextender?¶
Data is the lifeblood of modern businesses. Data-empowered organizations are:
- 23× more likely to acquire customers
- 6× more likely to retain customers
- 19× more likely to be profitable
However, data teams are constantly struggling:
- Data scientists spend 45% of their time on data preparation.
- 63% of employees report they can't get the insights they need.
- 25% of business experts simply guess when making important decisions.
The results are in: the "modern data stack" is not meeting the needs of modern businesses.
Organizations require fast access to reliable data for multiple use cases¶
To become data-empowered, data must be extracted from various source systems, cleaned, consolidated and curated into reliable datasets for business consumption. Large organizations have many use cases for data, which requires a three-tier architecture:
- Data lake
- Data warehouse
- Data marts
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…¶
| Question | The obvious answer |
|---|---|
| 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 I use Excel, Power BI or Tableau? |
| So, if data is so important and time-sensitive, why would you hand-code your data estate? | You can build your data estate like it's 1999 — but you don't have to. |
What is Timextender?¶
Timextender enables thousands of organizations to build a future-proof infrastructure for ingesting, preparing and delivering clean, reliable data in the fastest, most efficient way possible.
By unifying each layer of the data stack, we take a holistic approach to data integration that optimizes for agility — empowering you to build data solutions 10× faster and reduce costs by 70–80%.
| Capability | What it means |
|---|---|
| Universal connectivity | A directory of 250+ pre-built, fully managed data connectors, with additional support for any custom data source, so you get access to your data faster. |
| Lineage and documentation | Instantly generate end-to-end data lineage and documentation, to assure users their data is complete, accurate and reliable. |
| Agile, low-code | Automatically generate data cleansing and transformation code, allowing faster, less error-prone development. |
| Intelligent data pipelines | Since all object dependencies are fully managed, multiple pipelines run in parallel and are intelligently optimized by learning from past executions. |
| Future-proof | With solutions stored as metadata, best-practice code is generated specifically for the platform you choose — so you can switch to new modern cloud platforms without rebuilding. |
| Business-ready data | Easily create business-friendly data marts and deploy them to multiple analysis and visualization tools such as Power BI, Qlik or Tableau, so users can quickly build their own graphs, dashboards and reports. |
Timextender instances¶
Timextender solutions are built with the components of the data estate in mind — we just refer to them slightly differently.
| Data estate component | Timextender instance |
|---|---|
| Operational data exchange | Ingest |
| Modern data warehouse | Prepare |
| Shared semantic layer | Deliver |
When is Timextender used?¶
- Build new data infrastructure
- Modernize existing data infrastructure
- Prepare data for AI/ML
- Integrate data for mergers and acquisitions
- Deliver governed models for BI — and finally achieve self-service analytics
Who uses Timextender?¶
Thousands of companies in nearly 100 countries around the world use Timextender. Every industry vertical needs data, so any industry vertical can use Timextender — Heijmans, Komatsu, Pandora, Michigan's capital, and many, many more.
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 — see Enterprise Architectures for the detailed configuration guidance.
| Architecture | When to choose it |
|---|---|
| On-premises SQL Server | Build a fully on-premises data warehouse using Timextender. |
| Azure SQL | Cost-effective cloud solution for small to medium data warehouses. Ideal for production data warehouses smaller than 1 TB. |
| Azure Synapse | Highly scalable solution for enterprise-grade analytics. Ideal for production data warehouses larger than 1 TB. |
| Snowflake | Ideal where high performance, simplicity, concurrency and affordability are the pillars of the data warehouse design. |
| AWS RDS SQL | Ideal for a managed, scalable, highly available relational database service in the cloud, with simplified administration and robust security. |
Section quiz¶
Planning to take the Solution Architect exam? Then make sure these 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?
Answers¶
- Data lake, data warehouse, data marts.
- Build new data infrastructure; modernize an existing data warehouse; prepare data for AI/ML.
- On-premises SQL Server, Azure SQL, Azure Synapse.
Related reference¶
- What is Data Integration — the next step in the course.
- Enterprise Architectures — the reference architectures in detail.
- Install and configure — get your own environment running.
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