Using the ESB approach and Data Lake, we reduce the load on systems, eliminate data loss during exchange, simplify the modification of IT systems, and accelerate the analysis of all data.
Prevent data loss during the exchange
between systems
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During the point-to-point exchange, data loss is possible in case one of the systems fails: one system considers a message transmitted, whereas the second has not received it yet.
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We configure the integration in such a way that if the connection is broken, the message will be considered unprocessed and will be transmitted at the next pass
Change some IT systems
without changing the others
The old approach only allowed getting data from the source as it was. The consumer had to convert it to their own format. With such an approach, any change in the source would bring about a cascade of changes in all related data flows.
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Changing the ERP or any other system won't require adjustments in all connected systems. If a system changes, it will only affect the "source-storage" connectors. Everything else will remain unchanged for all consumers.
Reduce system loads without improvements
or increase in resources
Different systems need the same data. Consumers duplicate requests to the source system, thus increasing the load. If a company has one system for storing the main data, it will receive a huge number of requests. Very often this is an old monolithic system and a business wants to replace it in the future.
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Consumers turn to the storage. The load on the main source is reduced without any improvements to the system itself or increase in resources. When the time comes to replace the old system with several new services, it will be necessary to change the "source-storage" flows. The changes will not affect many consumers.
API as a separate service.
Connect uniform consumers without extra load
To connect multiple consumers of the same type (for example, retailers), an API is often created inside the system. At the same time, the load that these consumers create falls on the system.
The lack of access to the source system will become a problem for all consumers. They won't be able to get necessary data.
We create API connectors as standalone services, separate from the source system. They operate independently of the source system and can handle high loads. You will be able to serve systems without issues for a multitude of consumers.
BI implementation is made easy
with an existing Data Lake
By implementing ESB, we create a common enterprise data warehouse: Data Lake.
It is easy to connect any analytics system and upload reports to such a warehouse. All data is ready.
We build the warehouse architecture so that the generation of reports on a large array of information does not reduce the speed of daily data exchange.
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The monitoring system controls
the operation of each flow
We log key stages of each flow's operation. If an error occurs that requires action, you will receive a message on Telegram. It will include a description of the error and a link to more details. This allows you to proactively respond to incidents rather than waiting for user reports.
The support team operator will know exactly where and what went off-script, helping to resolve incidents faster. We can set up a monitoring system from scratch or configure monitoring in your infrastructure.
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The data bus (ESB) is a set of microservices,
not a monolith
With our approach, there is no single point of failure in the architecture. A data bus (ESB) is a set of microservices that are not interconnected.
ESB components are independent of each other and can run on different servers in different locations.
The exchange failure of any system will only affect this system. Other systems keep on working.
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We help solve data exchange issues between IT systems
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Critical data can be lost during exchanges (such as orders, prices, inventory, and more).
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Changes in one system lead to cascading changes in related systems.
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Systems can be overwhelmed by an excess of data and requests from both data sources and consumers.
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The team only learns about errors when problems reach the users.
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Connecting to multiple similar consumers (e.g., retailers and suppliers) in the exchange.
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The data exchange mechanism does not allow for quick integration with an analytics system and generating reports (DWH and BI).
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The architecture has a single point of failure. If one of the components fails, everything related to it also fails.
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Current tools cannot implement complex data transformation logic.
We use the best practices
and fundamental knowledge in IT
Prevent data loss during the exchange
between systems
During the point-to-point exchange, data loss is possible in case one of the systems fails: one system considers a message transmitted, whereas the second has not received it yet.
We configure the integration in such a way that if the connection is broken, the message will be considered unprocessed and will be transmitted at the next pass.
Change some IT systems
without changing the others
The old approach only allowed getting data from the source as it was. The consumer had to convert it to their own format. With such an approach, any change in the source would bring about a cascade of changes in all related data flows.
Changing the ERP or any other system won't require adjustments in all connected systems. If a system changes, it will only affect the "source-storage" connectors. Everything else will remain unchanged for all consumers.
Reduce system loads without improvements
or increase in resources
Different systems need the same data. Consumers duplicate requests to the source system, thus increasing the load. If a company has one system for storing the main data, it will receive a huge number of requests. Very often this is an old monolithic system and a business wants to replace it in the future.
Consumers turn to the storage. The load on the main source is reduced without any improvements to the system itself or increase in resources. When the time comes to replace the old system with several new services, it will be necessary to change the "source-storage" flows. The changes will not affect many consumers.
API as a separate service. Connect uniform consumers without extra load
To connect multiple consumers of the same type (for example, retailers), an API is often created inside the system. At the same time, the load that these consumers create falls on the system.
The lack of access to the source system will become a problem for all consumers. They won't be able to get necessary data.
We create API connectors as standalone services, separate from the source system. They operate independently of the source system and can handle high loads.
You will be able to serve systems without issues for a multitude of consumers.
Выгружайте данные один раз -
используйте сколько угодно раз в любых комбинациях
Новые данные выгружаются только один раз: из источника в хранилище. Потребители легко используют структурированные данные хранилища сколько угодно раз и в любых комбинациях.
Например, Рознице N нужны “оплаченные заказы”. Данные о заказах и оплатах передают в хранилище разные системы.
Коннектор найдет в хранилище “заказы” и “оплаты”, подходящие для Розницы N. Далее соберет данные в сообщение и передаст потребителю.
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BI implementation is made easy
with an existing Data Lake
By implementing ESB, we create a common enterprise data warehouse: Data Lake.
It is easy to connect any analytics system and upload reports to such a warehouse. All data is ready.
We build the warehouse architecture so that the generation of reports on a large array of information does not reduce the speed of daily data exchange.
The monitoring system controls
the operation of each flow
We log key stages of each flow's operation. If an error occurs that requires action, you will receive a message on Telegram. It will include a description of the error and a link to more details. This allows you to proactively respond to incidents rather than waiting for user reports.
The support team operator will know exactly where and what went off-script, helping to resolve incidents faster. We can set up a monitoring system from scratch or configure monitoring in your infrastructure.
The data bus (ESB) is a set of microservices,
not a monolith
With our approach, there is no single point of failure in the architecture. A data bus (ESB) is a set of microservices that are not interconnected.
ESB components are independent of each other and can run on different servers in different locations.
The exchange failure of any system will only affect this system. Other systems keep on working.
We use proven international open-source products
We use Opensource solutions. Therefore, our clients reduce their licence fee costs without the risk of restrictions imposed by the legislation of different countries.
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A simple and flexible low-code platform that is part of the Salesforce company with revenue of more than $ 30 million per year. We use the Community Edition version.
How we use it
Graphic Studio to create connectors (ETL).
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An open source DevOps lifecycle web tool. More than 30 million registered users.
How we use it
Version Control and setup of access rights (roles).
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Solutions for corporate security, surveillance and search built on the Elasticsearch platform used by thousands of companies.
How we use it
To store, analyse and search by logs
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An information visualization and analysis system that allows you to work with a wide range of data sources out of the box. More than 20 million users.
How we use it
Dashboards with information about the status of streams.
Создаем потоки со скоростью
на грани ваших возможностей
Создаем унифицированные коннекторы, сохраняя гибкость для решения уникальных задач. Часто скорость разработки ограничивается только возможностью заказчика предоставлять данные о системах.
Вам необязательно иметь в штате аналитика. Мы проведем интервью, сами спроектируем коннекторы и согласуем с вами на наглядных схемах.
110+
специалистов в штате
50+
клиентов из числа крупного
и среднего бизнеса
30+
проектов по внедрению ESB
200+
работающих интеграций
на разных инструментах
Our standard procedure
of transition to ESB
We have built the ESB implementation process so that you get the maximum benefit.
You can go all the way through the implementation with us.
Or order any stage of the process separately, and impart the rest to your team.
1. Designing a loosely coupled architecture
You will receive a plan for transitioning to ESB, taking business specifics into account
2. Transfer of the most loaded streams to ESB
You will get a solution to 80% of the problems of data exchange between systems.
3. Transfer of other streams with a given speed and volume that is convenient for you
You will get a single exchange mechanism for the entire enterprise and a reduction in maintenance costs.
Our standard procedure
of transition to ESB
We have built the ESB implementation process so that you get the maximum benefit.
You can go all the way through the implementation with us.
Or order any stage of the process separately, and impart the rest to your team.
1. Designing a loosely coupled architecture
.webp)
You will receive a plan for transitioning to ESB, taking business specifics into account
2. Transfer of the most loaded streams to ESB
.webp)
You will get a solution to 80% of the problems of data exchange between systems
3. Transfer of other streams with a given speed and volume that is convenient for you
.webp)
You will get a single exchange mechanism for the entire enterprise and a reduction in maintenance costs
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