Top 5 Challenges When Implementing a Data Lake and How to Overcome Them

At Synetec, many of our clients come to us with issues regarding data lake implementation.  George Toursoulopoulos, CEO at Synetec, explores the top five challenges companies often face and provides insights on how these challenges can be effectively addressed.

A data lake is a centralised repository that allows companies to store all of their structured and unstructured data at any scale. Companies can store this data ‘as-is’ (without having to structure it first) and then run different types of analytics. This can range from dashboards and visualisations to big data processing, real-time analytics, and machine learning—to drive better decision-making.

Despite the clear benefits, companies can encounter significant issues when data is not properly managed. Here we outline the primary challenges that often arise, and provide solutions to help companies overcome them:

1.     Data Volume Management

• Challenge: Handling and processing large volumes of data efficiently. Initial processing can reveal the need for careful consideration in programming database queries and save procedures to prevent inefficiencies. Getting this right in the beginning is critical to avoid further problems.
• Solution: Implement efficient data aggregation and transformation processes, including pre-processing steps and grouping final data to manage storage and prevent duplication. Partitioning and distributed processing frameworks would make the solution scalable, which is crucial.

2.     Data Source Integration

• Challenge: Consolidating data from diverse sources with varying structures and formats. This is a common challenge that is faced by many of our clients.
• Solution: Develop custom solutions for each data source and adopt the use ETL tools or data integration platforms that offer pre-built connectors and pipelines for diverse data sources.

3. Technical Expertise and Requirements

• Challenge: Addressing the client's lack of technical expertise, leading to unclear instructions and frequent code rewrites.
• Solution: Cross-functional collaboration (between business and technical teams), agile methodologies, and investing in training and up skilling can ensure a shared understanding of data and technology.

4. Data Accuracy/Quality and Consistency/Cleaning

• Challenge: Ensuring data accuracy and reliability by reducing manual handling errors and standardising data units and date-time formats.
• Solution: Implement automated data collection, extraction, and transformation processes to minimise human intervention and errors. Use data validation     techniques which ensure that the data flowing into the lake is of high quality and build automated data ingestion pipelines that can handle diverse data sources.

5.     Infrastructure and Performance Optimisation

• Challenge: Optimising infrastructure to handle significant data processing needs without impacting performance.
• Solution: Azure Data Lake Storage (ADLS) or Azure Synapse Analytics would be appropriate for handling large-scale, unstructured, and semi-structured data, commonly found in data lake environments.

Beyond these five primary challenges, there are additional critical aspects that clients should consider:

·      Data Governance and Security: Establish strong data governance policies to manage data access, security, privacy, and compliance, which is vital for ensuring the protection of sensitive data and adherence to regulations.  Data lineage (tracking the origin of data) and auditing mechanisms ensure proper tracking of how data is used, especially in regulated industries.

·      Scalability and Future-Proofing: Using best practices like horizontal scaling, micro services architecture and containerisation to ensure your data lake can handle growing data volumes, evolving business needs, and future technology shifts. We look at your long-term data volume, types of data, and user access needs. We choose the right data storage architecture and a flexible cloud-based platform. These challenges and solutions highlight the complexity and necessary strategic planning required for a successful data lake implementation. At Synetec, we drive better business decisions by providing your business with a centralised, flexible, and scalable environment.

Contact us to learn more.

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