The increasing appeal of data lakes stems not only from the business drivers fueling their growth but also from the cloud’s capacity to provide extensive storage and processing power at progressively lower costs. As a result, organizations of all sizes are turning to data lake platforms.
The IT community remains captivated by data lake implementation. According to a recent Research and Markets analysis, the data lake market is projected to achieve a compound annual growth rate (CAGR) of 26%, reaching $20.1 billion by 2024.
If your organization is planning to embark on a data lake implementation, here are important aspects to take into account.
Understanding a Data Lake
A useful way to comprehend data lakes is by comparing them to data warehouses. Both are designed to store large volumes of data, but their differences are significant.
Unlike data warehouses, where data is ingested with a predefined purpose, data lakes allow data from various sources to be stored without such constraints. Analysts utilize this data to explore, experiment, and uncover its potential benefits and applications. Data warehouses, on the other hand, require a meticulous evaluation of input sources before ingestion.
Data lakes also differ in their approach to schema application. In data warehouses, schemas are applied before data ingestion, whereas in data lakes, schemas are imposed after ingestion. Furthermore, data lakes store information in its raw state, simplifying the ingestion process compared to the structured processing in data warehouses.
Data lakes excel in accommodating structured, semi-structured, and unstructured data and are well-suited for handling streaming data alongside batch processes. While data warehouses can manage diverse data types, their primary focus is structured data via batch ingestion.
Initial Steps to Implement a Data Lake
The first step in data lake implementation involves gaining a thorough understanding of its architecture, platforms, products, and workflows by leveraging vendor resources and other informative materials.
To select the best platform, a detailed evaluation of the available options is essential. Below are some criteria to guide your analysis:
- Technology:
A wide range of data lake platforms is available, including solutions like CloudSpace’s data lake analytics based on Microsoft Azure. These platforms offer scalable and secure solutions for data storage and analysis. - Security and Access Control:
Protecting data lakes from unauthorized access is critical, as they house valuable business insights. - Data Ingestion:
Evaluate the platform’s ability to ingest structured, semi-structured, unstructured, and streaming data efficiently, regardless of the batch size. - Metadata Management:
Metadata is vital for locating and understanding data within the lake. Assess how the platform captures and manages metadata. - Performance and Scalability:
Consider the tools available for user interaction, data exploration, and operational efficiency. Analyze the platform’s speed and scalability to ensure it meets workload demands. - Management and Monitoring:
A strong user interface for administration, monitoring, and workload management is essential. - Data Governance:
The platform should offer robust mechanisms to maintain data consistency, accuracy, and reliability while supporting sandbox environments for experimentation. - Data Analysis and Accessibility:
Look for features enabling advanced data analysis, machine learning integration, and seamless use of third-party tools. - Costing Strategies:
Understand the vendor’s pricing structure.
Steps for Data Lake Implementation
Once a platform is selected, the focus shifts to developing the organizational framework, processes, and procedures necessary to manage and analyze data effectively.
Key actions include:
- Assemble Expertise:
Hire experienced professionals and train your team to navigate the complexities of data lakes. Define roles and reporting structures aligned with the implementation. - Develop a Strategy:
Create a project plan outlining goals, milestones, and evaluation criteria for the data lake’s success. Incorporate data classification standards for storage and archiving. - Prioritize Data Sources:
Identify potential data sources and prioritize them based on their significance to the organization. Data currently undergoing analysis may hold lower priority than data from unexplored systems. - Ensure Governance:
Implement and enforce strategies to maintain data security, consistency, and accuracy. - Standardize Analysis Processes:
Establish flexible yet standardized methods for data exploration and experimentation. Data scientists should use these processes to determine valuable use cases that drive business outcomes. Target outputs may include other BI platforms and business applications.