We are in the digital era, where companies handle large volumes of data from internal systems, cloud applications, platforms, among others. In order to give value to this data, it is essential to achieve data effective data integration especially in complex environments.
If you are looking to understand the challenges involved in integrating data across different environments and know what technology solutions help facilitate this process, then the following article from PREDIQT will be of great help to you. We will show you advanced Artificial Intelligence and Machine Learning tools that allow an easier, safer and scalable integration..
What is data integration?
Data integration refers to the process of bringing together data from multiple sources across an organization to provide a complete, accurate and up-to-date data set for BI, data analytics and other business applications and processes.
It includes data replication, ingestion and transformation to combine different types of data into standardized formats to be stored in a target repository, such as a data warehouse, data lake or data lakehouse. By having the information unified, this practice becomes key to making informed data-driven decisions. informed data-driven decisions and creating evidence-backed strategies.
Integration challenges
The data ecosystem has become more diverse and distributed in recent years, as enterprises are adopting cloud solutions or making use of new technologies such as AI. Some of the main reasons that make it difficult to data integration today are:
- Wide variety of sources: Information can come to be found in cloud services, external APIs, mobile apps, local data, etc.
- Constant updates: As systems undergo constant changes and evolve, this makes it necessary to update integrations frequently.
- Heterogeneous formats: Many files are not always compatible with each other. For example: CSV files, JSON, XML, relational and non-relational databases, structured and unstructured data. These formats need different treatment in order to be unified with the others.
- Quality problems: Duplicate, incomplete or inconsistent data make it difficult to unify and analyze.
- Scalability: As the enterprise grows, so do its data volumes. Integration solutions must be able to scale without losing performance or compromising security.
5 data integration approaches
There are five different approaches, or patterns, for executing data integration: ETL, ELT, streaming (transmission), application integration (API) and data virtualization.
To implement these processes, data engineers, architects and developers can either manually code an architecture using SQL or, more commonly, configure and manage a data integration tool, which streamlines development and automates the system.
1. ETL
An ETL pipeline is a traditional type of data pipeline that converts raw data to match the target system through three steps: extract, transform and load.
The data is transformed in an intermediate area before being loaded into the target repository (typically a data warehouse). data warehouse). This enables fast and accurate data analysis in the target system and is most appropriate for data sets that require complex transformations.
2. ELT
In the more modern ELT data pipeline, data is loaded immediately and then transformed within the target system, typically a data lake. data lake, data warehouse o data lakehouse in the cloud. This approach is most suitable when data sets are large and timeliness is important, as loading is usually faster.
3. Data Transmission (Streaming)
Instead of loading data into a new repository in batches, streaming data integration moves data continuously and in real time from source to destination. Modern data integration platforms can deliver analytics-ready data to streaming platforms in the cloud, data warehouses y data lakes.
4. Application Integration
Application integration (API) allows separate applications to work together by moving and synchronizing data between them. The most common use case is to support operational needs, such as ensuring that the HR system has the same data as the financial system. Therefore, application integration must provide consistency between data sets.
5. Data Virtualization
Like streaming, data virtualization also delivers data in real time, but only when requested by a user or application. Still, it can create a unified view of data and make it available on demand, virtually combining data from different systems. Virtualization and streaming are suitable for transactional systems built for high-performance queries.
Conclusions
The data integration is one of the most important pillars for companies wishing to advance their digital transformation. Overcoming the challenges involved in this process makes it possible to harness the true potential of data: generating value, making smarter decisions and optimizing processes at all levels.
En PREDIQT, entendemos que una integración de datos exitosa requiere planificación estratégica y ejecución experta. Desde la evaluación inicial hasta la implementación, te acompañamos en cada etapa. Agendemos una reunión y
diseñemos juntos una solución segura, escalable y alineada con los objetivos de tu negocio.
Frequently asked questions about Data Integration in complex environments: Challenges and Solutions
Why rely on a technology partner for data integration?
Implementing an integration strategy not only requires tools, but also technical expertise, strategic vision and business knowledge. A specialized provider will help you design a scalable architecture, select appropriate technologies, automate processes efficiently and comply with quality standards.
What is a data warehouse?
A data warehouse is a centralized system that stores large volumes of data from various sources to facilitate their analysis. It is optimized for quick queries and strategic decision making. Unlike operational databases, it focuses on historical and consolidated analysis of information.
How do I know if my company needs to implement a data integration strategy?
Your company may need a data integration strategy if you manage information in different systems that do not communicate with each other. If you're having trouble getting unified reporting, making data-driven decisions, or ensuring the quality and consistency of information, it's time to consider a solution.