The adoption of data lake solutions has doubled over the last three years. It is clear that the technologies and best practices surrounding data lakes are constantly changing, as are the challenges.
Modern businesses cannot function without data. We now live in a world saturated with information due to the astounding rate at which information is generated, recorded, and churned every year. Data analysis can be transformed into a strategic decision-making process using modern data platforms and advanced technologies such as machine learning, business intelligence, and artificial intelligence. As a result, a unified data repository or a Data Lake can be created across the enterprise and made available for analysis to everyone in the organization in order to address the needs of data access, scalability, and flexibility anywhere, anytime. Based on the new edge technologies, the Data Lake creates a Modern Data Platform that resolves the scalability issue and helps to resolve the high costs and exponential growth of data.
But, one of the most common challenges that organizations face is creating a Data Lake that is efficient and impactful for the business. Data Lakes are prone to failure on numerous occasions. In 2018, Gartner estimates that 80% of Data Lakes will have no effective metadata management features, which will contribute to their inefficiency.
The following points are critical to building an effective data lake modernization platform:
The pattern of data access:
With a special focus on future-state data models, the way data is stored is fundamental. A Modern Data Platform that is user-friendly and inherently analytics-enabled is essential for an organization to succeed in this fast-paced data-driven business world. An understanding of the data access pattern is crucial to providing a user-friendly platform, which can mean adopting a future-state Data Model. This process can be very challenging and cumbersome when done manually. Major failures can occur when upgrading the data platform. As part of their rich technical expertise and investments, Datametica is developing intelligent technology, called Eagle, to provide automated analysis of data access patterns across data stores, which recommends a model and platform for the future. Furthermore, Eagle assists in tuning and optimizing data access patterns, data levels, and platform performance.
In order to modernize legacy data systems to a Modern Data Platform Data Lake, the limitations, expense, rigidity, scalability, and responsiveness of legacy systems must be addressed. As part of this initiative, Datametica recommends a platform with services such as serverless architecture, managed services, local data storage, decoupling of data storage and computation, cost transparency, a pay-per-use model, and analytics library in the future state.
Establishing a strong foundation:
To build a solid foundation for Data Lakes, protocols, processes, and tools need to be defined. There are several aspects that need to be decided once the data platform is prepared and the optimized data model is optimized, such as which tools and technologies are to be used, how security will be achieved, the computational processes, setting up the serving layer, etc. In summary, the data lake foundation needs to be laid out even before the first use case is performed.
Various data sources such as databases, CDC, and logs can be integrated into an ingestion framework to ensure optimal data absorption in Data Lakes. It is performed in the ingestion layer, which will be capable of handling a variety of data patterns such as batch, micro-batch, and real-time ingestion. In order to integrate data from various data sources, such as databases, file systems, and data streams.
Modernizing the workload:
As soon as the data is in place, enterprises should focus on modernizing the workload running on the new data lake. When onboarding the workload, it may become necessary to convert the code if the code of your existing platform and that of the future state differ.
Data should be profiled to detect anomalies. Optimizing data sets requires understanding the taxonomy of data. It is ensured that the curated data is migrated to the Data Lake by continuous data validation and direct workload comparison between the existing environment and the Data Lake.
Data is always accessible and easy to track! You understand the importance of keeping data safe when you are living on the basis of data in the business world. Data from multiple sources introduces new vulnerabilities. Data Lakes should be kept secure to protect the sensitive data offloaded from legacy systems. Business-critical data is protected by a wide range of methods, including data encryption (at rest or in transit), data masking, security of data ingestion, access control, authentication and authorization, access node protection, firewall installation, and more.
Data Governance solution, which establishes efficient metadata management, data quality, and end-to-end data auditing and lineage. In this manner, major gaps and functionalities are filled, allowing data to be defined consistently across the enterprise. An intelligent, collaborative, automated platform that helps organizations navigate data at various levels.
Therefore, businesses need to invest in not only the powerful automation, security, and governance capabilities of IT, but also user-friendly features like 'search and publish' to ensure self-reliance among their users.
Enterprise data lakes will only become more innovative as they evolve and mature as time and technologies progress. As a result, organizations need to pay attention to the aforementioned factors so that data lakes can offer a trusted, analytics-ready source of data. As a result, they will deliver business value sooner not only to internal stakeholders but also to their customers.
Polestar Solutions improves the value proposition for the data by finding new ways to utilize it and minimizing the pain points. Cloud and Big Data resources are used extensively to develop well-governed Data Lakes, as well as high-performance framework solutions and best practices for both cloud and on-premise architecture.