Outperform & Digitalize your
Business Decisions with
a Scalable Data Lake

Atreus Global in partnership with Datametica assists companies by setting up their Data Lake.

It is a fact that businesses that implemented Data Lakes outperformed similar companies. With larger chunks of information collected from various sources, the need to leverage this information and help make better informed business decisions arises.

This has enabled businesses to identify and implement opportunities, helping companies to grow faster, specifically, in terms of productivity, attracting and retaining customers, making informed decisions and more.

Benefits of Data Lake

Benefits of Data Lake

Does your Business need the Data Lake ?

Data Warehouse
– Only stores processed data which has specific use within the company.​
– Storage spaces cannot be wasted on data that may never be used.

Data Lake
– Data stored is unknown.​
– Most data can stored for future use (even not used).​
– Store ‘everything’ approach.

Data Warehouse
Historical analysis.

Data Lake
– Real time business information.​
– Discovery of ideas for business growth.​
– Connecting machinery in factory for live data of lines, equipment.​
– New normal due to its many business benefits.

Data Warehouse
Used by business professionals in forms of tables, charts, spreadsheet, and others. Almost all employees in a company can read processed data.​​

Data Lake
– Business professionals for real time analytics reports for business decisions.​
– IT professionals for translating unprocessed data for management or other employees.​
– Share data enterprise-wide.

Data Warehouse
– Structured data making the data easier to search and find.​
– Limitation of data makes it more costly to manipulate.​

Data Lake
Easy to use and change because of the lack of structure.​

Data Warehouse
– Difficult to change because processed data.​
– Considerable amount of time need to be spent in developing the warehouse structure.​

Data Lake
Accessible to anyone who needs the data.​

Data Warehouse
Data extracted from transactional systems (traditional).

Data Lake
– Embraces nontraditional data types (web server logs, sensor data, and social network activities, among others.​
– Keeps all forms of data regardless of the source and structure.

Data Warehouse
Scales to moderate volume with a high cost.​​​

Data Lake
Scales to huge volumes at very low cost.​

Data Warehouse
Efficiently uses CPU but high storage and processing costs.

Data Lake
Efficiently uses storage and processing at very low cost.​​

Data Warehouse
Easy to control data security and data cleanliness.

Data Lake
Requires metadata driven approach to enable quality, security and privacy.​​

Data Warehouse
Store processed and refined data.

Data Lake
Store raw data which purposes might be unknown​.

Data Warehouse
– Transform once.​
– Requires IT assistance that can take weeks.

Data Lake
– Adhoc transformations (as and when required).​
– Self Service transformations on the fly.

Data Warehouse
Data needs to be transformed before having access to it which could take days and in some cases, weeks.​

Data Lake
– Access data before it been transformed​
– Enables users to get a fast result.