Every technological innovation often becomes more accessible and scalable the older it gets. Today’s big data analysis operations leveraging vast databases in the cloud demonstrate why businesses must recognize the value of investing in data warehouses or lakes. This post will elaborate on how data lakes are used in big data analytics to improve business intelligence and decision-making.
What is a Data Lake?
Data lakes contain intelligence resources in unprocessed form to facilitate multipurpose ease of access. Therefore, each professional can request the data in its original format and devise unique services appropriate to a departmental goal. Several data lake services also integrate, offer, and periodically upgrade data architecture, streamlining customization activities.
Unlike a data warehouse, a data lake is used in big data and analytics to empower data scientists, engineers, and analysts to handle raw data efficiently. It can depend on artificial intelligence (AI) and large language models (LLMs) to reduce the manual work the analysts must do to discover business-critical insights.
The Importance of Data Lakes in Big Data Analytics
Big data analytics involves datasets that keep growing in volume, necessitating an IT ecosystem that can support the data storage needs while ensuring data quality. So, analytics consulting services utilize data lakes to benefit from big data and mitigate technology risks via cloud-powered computing virtualization.
Likewise, data lakes can assist managers in increasing interoperability between an organization’s legacy systems and modern operating environments. After all, every data lake must follow design and formatting standards, delivering the best cross-platform data access, storage, and transfer compatibility. Otherwise, the client is risking being in a vendor lock-in.
At the same time, user-friendliness across a data lake application interface is as significant as a robust data governance framework. Data lakes are important because they securely hold structured and unstructured data. Besides, data lake providers frequently test and update the client experience element to eliminate redundancies in categorization, validation, insight extraction, and reporting.
How Data Lakes Are Used in Big Data Analytics?
Use 1 – Accelerating Data Gathering
Data lakes focus on collecting and storing raw data files in their initial format. This approach enables big data analysts and engineers to postpone database structuring, content categorization, and data quality management (DQM) tasks. It also helps avoid the need for human supervision in the initial stages of data gathering if AI tools are integrated.
Use 2 – Scaling Storage and Data Pipelines
Big data analytics used for social listening, news monitoring, and market research requires continuous data acquisition. So, data professionals cannot keep using the same data storage system. They must get the flexibility of data lakes because the supported cloud platforms offer straightforward resource virtualization. And enterprises can reduce or increase the storage and processing power when their big data needs evolve.
Use 3 – Building Central Repositories
Data duplication issues and the latest reports relying on obsolete business intelligence are two examples of how maintaining multiple “local” versions of the same data resource creates trouble.
Data lakes allow analysts to develop adequately consolidated data sources available to anyone with a stable internet connection. So, employees can modify a data element while their colleagues will immediately know about these changes through alerts, authentication requests, and file version controls.
Conclusion
Data quality assurance during data collection might slow the social listening and product reception studies. However, brands can make the process faster by reordering DQM tasks such that they occur after extensive data becomes available. And data lakes can help companies manage those raw data objects.
An enterprise-grade data lake provides governance and automation features. Since it is a central data repository, managers must ensure appropriate cybersecurity measures are in place.
Furthermore, the underlying data lake techniques must be compatible with the company’s IT resources. Otherwise, data loss risks might decrease your operational and worker effectiveness. These requirements make developing data lakes or using them for big data analytics more challenging, proving the need for the right attitude and skillset.
No comments yet