DATA & INFRASTRUCTURE
Data warehousing: centralized analytical data, without a cluster
Data warehousing with TIMi centralizes your analytical data in compressed columnar files, queried by the same engine that prepares and models it, on a standard server instead of a cluster.
Central storage
One place for the data your analyses need.
Columnar files
Reads only the columns a query needs.
Standard hardware
Vertical scaling first, no cluster required.
HOW IT WORKS
How data warehousing works.
Three steps, in one engine, on a standard server with no cluster.
1. Load the data
Anatella gathers data from your sources and loads it into its own file format.
2. Store by column
In the columnar format, a query that needs 4 columns out of many reads only those 4, which is why business intelligence tasks run faster.
3. Query and reuse
The same data feeds dashboards, reports and predictive models, without copying it between tools.
AnatellaGETTING IT RIGHT
Choices that shape an analytical store.
Decide what the store is for before choosing where the data lives.
Analytical store, not operational system
The columnar files are built for analysis. Operational systems with concurrent transactions stay on a relational database, which Anatella reads and writes through ODBC and OLEDB.
Size to the use
Aggregates over a few columns benefit most from the columnar format. In our documentation, such tasks run from five to a hundred times faster than in the row-based format, at the cost of slightly more memory.
Plan scaling in steps
Vertical scaling on a larger server comes first. Horizontal scaling remains possible without rewriting your flows.
TRANSPARENCY
A store you can audit.
Data stays on your own servers, in files you control. Loading and transformation steps are flows you can read, so the origin of every table is documented.
For security teams
Data stays on your servers, with no cloud dependency.
For data teams
Each table traces back to the flow that built it.
For infrastructure
A standard server and a small footprint, instead of a cluster.
DATA
What a warehouse typically holds.
Anatella connects to more than 80 sources, so this data can be gathered without prior custom development.
Customer data
Profiles, contracts and interactions.
Transactions
Sales, payments and movements.
Sensor and event data
Streams and logs.
Reference data
Products, locations and organization.
PROOF
Volumes handled.
We do not have a published case study dedicated to data warehousing. The references below are indirect: they show the volumes Anatella handles on standard hardware.
30 TB and 400 million records per year
Not documented as a warehousing case. It shows the data volumes managed with Anatella.
150 billion transactions per month, on one server
Snype processes 150 billion transactions per month in 5 to 7 hours, on a single standard server.
FAQ
Common questions.
Does TIMi replace our enterprise data warehouse?
It depends on your needs. It can serve as the analytical store for analysis and modeling. For operational reporting with many concurrent users, evaluate it case by case.
Where is the data stored?
On your own servers, in files you control. There is no cloud dependency.
How does it scale?
Vertically first, on a larger standard server. Horizontal scaling is possible without rewriting your flows.
Can we keep our current database?
Yes. Anatella reads and writes relational databases through ODBC and OLEDB, so it can sit alongside them.
RELATED USE CASES
Keep exploring.
SEE FOR YOURSELF
Test it on your own data.
Run TIMi on your own data with a free trial, or talk to us about your case.
