Concepts such as Data Lake, Data Warehouse, Data Mesh and Artificial Intelligence (AI) are transforming the way organizations store, analyze and manage their information. Based on this, we separate the emerging trends in data architecture and share how they can boost your company's data structure.
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Data Lake and Data Warehouse: the foundation for flexible and structured data
Two structures that are the basis for data storage in many companies are the Data Lake and the Data Warehouse.
While the Data Lake allows you to store data in its raw format, promoting flexibility, the Data Warehouse is geared towards structured data analysis, suitable for insights and projections. The advantage of the Data Lake is the possibility of [https://dbtodata.com/uk-whatsapp]uk number for whatsapp[/url] storing diverse data in different formats, allowing for a comprehensive analytical approach. On the other hand, the Data Warehouse is ideal for long-term analysis, as it organizes structured data in a consistent way. In this way, both architectures can be complementary and meet the specific needs of different areas of the company.
Data Mesh: decentralization for greater agility
The Data Mesh concept emerges as a solution to the centralization that limits scalability and agility in data operations. This approach allows different business areas to manage their own data, which improves autonomy and facilitates the rapid fulfillment of specific needs.
According to Carlos Pedrosa, IT Superintendent at Banco Inter, the implementation of a decentralized structure has allowed the bank to respond quickly to demands in areas such as investments, insurance and marketplace, serving more than 33 million customers. This structure not only promotes scalability, but also improves the customer experience and facilitates data management by sector.
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Democratization of data
Democratizing data allows employees from different levels and areas to access data, even without in-depth technical knowledge. This eliminates bottlenecks and promotes a data-driven organizational culture. At Banco Inter, the implementation of the DEDE (Data Engineer Developer Environment) environment is a successful example: the tool allows employees to access and manipulate data easily, even without being SQL experts. This accessibility democratizes the use of data, allowing teams to have up-to-date information to make quick decisions aligned with market reality.
Real-time decisions supported by Analytics
The ability to analyze data in real time is a growing need in industries such as finance and customer service, where agility can be a competitive differentiator. Real-time data analysis allows companies to adjust strategies and make decisions as data is generated. To implement this trend, a robust data architecture is required, prepared for the continuous and agile processing of large volumes of information, which requires investment in cutting-edge technologies and DataOps practices.
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DataOps and the continuous integration between development and operations
DataOps stands out as an innovative methodology by integrating data-driven development and operations practices , improving data quality and collaboration between teams.
By promoting continuous automation and monitoring, DataOps reduces errors and increases the reliability of processed information. This practice is essential to ensure that data flows smoothly through systems and is ready to feed real-time analysis and predictive models, directly impacting business performance and results.
Data security
With increasing regulations and growing concerns about privacy, data security is a priority for companies that manage large volumes of sensitive information. Technologies such as encryption and multi-factor authentication are essential to protect data and ensure compliance with regulatory standards. In the case of Banco Inter, the creation of specific frameworks for compliance and data security in machine learning and generative AI processes strengthens governance and reduces risks, preserving the integrity and trust in data.
Read also: AI and Machine Learning Integration
These emerging trends are critical for companies looking to optimize their data strategies and align with market best practices. To continue evolving in data management and better serve their customers, it is essential to invest in an architecture that balances innovation, decentralization, and security, creating a solid and future-proof structure.