Using the ESB approach to data exchange, we create a common enterprise storage to quickly connect analytics and reports.
You'll also get
Comparison table
types of integrations
Data Warehouse
Data Warehouse is a complex of structured data from the entire enterprise.
It consists of warehouses for various objects (orders, prices, goods, etc.). The data structure is determined in advance. After all, businesses know what systems are and how they are used for the purposes of this.
Repositories independent of each other and can run on different servers in different locations. So the refusal of any of them will not cause problems with the rest.
Data Warehouse allows it is easy to connect any analytics system.
It is easy to filter data by open fields and search for the objects you need.
Data Warehouse improves data exchange between IT systems:
1. It reduces the load on the systems;
2. It weakens the coherence of system changes;
3. It allows you to aggregate and transform data however you like.
Learn more about integrations here.
Data sharing and analytics
for the purposes of the present day
Structured data,
understandable to all consumers
Data processing before uploading
in storage
Data Lake
What is Data Lake? Let's look at a simple example of an order management system (OMS).
For example, the OMS system keeps a history of data on the status of all orders. This data is important for analytics, but it only clogs the system itself. This amount of historical data nicer put it into shared storage, so that do not overload the system.
It is impossible to take into account all the formats for using this data in the future. Today we need a report on the amount of time it takes for an order to move from one status to another. After a month, you will need to use order data in a different way and in a different format.
To Data Lake is created to store data for future use - enterprise data lake. Data enters such a repository as it is. When it becomes clear what to use this data for and how, analysts will be able to start processing it.
Data storage and processing
for future use
Structured
and unstructured data
The data is processed by an analyst
after uploading
The difference between DL and DWH
for the purpose of implementation
In practice, the boundary between storage types is arbitrary. It all depends on the quality of implementation and further use.
Collect as much data as possible
for the purpose of future processing.
Collect data and make it
suitable for immediate consumption.
We create
fault-tolerant
data warehouses
1.
With our approach, the architecture does not have a single point of failure
2.
Storage components are independent of each other and can run on different servers in different locations
3.
We create duplicate vaults on several servers. If one server fails, the data stream will be redirected to another server
4.
You can use both cloud servers and on-premise
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We use Opensource solutions, so our customers reduce license fees without the risk of restrictions from legislation in different countries.
A simple and flexible low-code platform that is part of Salesforce with revenues of more than $30 million per year. We're using the Community Edition.
An open source, web-based DevOps lifecycle tool. More than 30 million registered users.
A powerful open-source object-relational database system that has been actively developed for over 35 years.
Enterprise security, surveillance, and search solutions built on the Elasticsearch platform used by thousands of companies.
A system for visualizing and analyzing information that allows you to work “out of the box” with a wide range of data sources.
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