Data Lake Best Practices: The Do’s and Don’ts

In today’s data-driven world, data lakes have emerged as the data architecture of choice when storing and analyzing large volumes of data. However, implementing a successful data lake requires diligent planning and design, as it can quickly become a data swamp with no additional value.

This blog post will delve into data lake best practices, including data governance, security measures, data quality considerations, and optimizing data management within a data lake. We will also discuss some common pitfalls to avoid when implementing a data lake, which brings the risks of turning a data lake into a failed project.

Data Lake Overview

A data lake is a data storage architecture focused on storing large volumes of data in a centralized repository. It represents a suitable storage architecture for meeting modern big data requirements due to the following characteristics:

• Flexible data storage formats – The possibility to store both structured and unstructured data in their original format (JSON, CSV, or something else) allows organizations to quickly capture data collected from source systems without spending too much time and resources on additional conversions and transformations (the so-called Extract-Load-Transform method, as shown in Figure 1). 
• Centralized data storage: Data Lakes such as Databricks, Amazon S3, etc. provide centralized data storage for creating a single source of truth.
• Scalability – Designed to scale easily with the growing volumes of data to be stored (petabytes and more).
• Versatility – Support for various data analysis applications (data analytics, machine learning, business intelligence, etc.)

Therefore, in modern organizations, data lakes represent the preferred storage option for analyzing large amounts of data for various purposes (sometimes unknown upfront).

Top Data Lake Best Practices (in detail)

1. Planning for the data lake

Before implementing the data lake, it is important to understand its requirements and purpose clearly. This global view of the data lake is closely related to specific requirements for data types to be stored in the data lake, the frequency of data ingested from source systems, and data access and processing patterns. We must also be aware of any potential data governance requirements and data-related regulations we must comply with. 

2. Choosing the right tools

The modern data market has different tools and technology for building data lakes. Since data lakes were primarily introduced as cloud data solutions, most vendors offer different cloud-based solutions that differ in costs, complexity, and level of control. The ultimate decision often comes down to the available budget, expected data volume, processing requirements, and overall data infrastructure within an organization. 

3. Data ingestion optimization

Within the data lake pipeline, data flows from source systems to dashboards and other data consumption tools and services accessed by users. Therefore, it is crucial to detect potential bottlenecks within the data pipeline that have a negative effect on subsequent activities. The data ingestion stage frequently slows down the data lake performance, as we have to find the optimal tradeoff between latency, performance, and cost between the data lake and external source systems.

4. Parallelization in data management

Data lakes achieve their purpose only if they are able to provide the requested information to users within a sensible time interval. Hence, we must consider data access patterns when optimizing queries on data. A good practice is to implement parallelization into all stages of the data lake pipelines. In the end, distributing computational workloads on large data volumes across multiple nodes speeds up complex analytical queries, data cleaning and transformation tasks, and many more.

5. Use of data catalogs 

A data catalog provides a central repository of metadata and descriptions of data stored in a data lake. It acts as a source of truth used to support data discovery, search, and access by data lake users. Proper and up-to-date maintenance of the data catalog by stakeholders ensures data quality and governance. 

6. Employing data quality and governance mechanisms

In practice, poor data quality and governance mechanisms frequently lead to failed data lake implementation projects. The so-called D-WATER method (Data catalog for recording metadata, Workflows for data processing, employing strict Access Control, properly Tracking changes to data, Encryption of data for increased security, and increasing data Reliability by continuously monitoring data quality) can be used to mitigate potential data quality risks. 

7. Securing data access with access controls

Data security and compliance are critical requirements for any data lake implementation. Some good practices in this context include the use of encryption to protect stored data against potential data leaks, extensive Access Control Lists (ACLs) ensuring data is accessed by relevant stakeholders only, and audit trails ensuring data lineage and provenance.

8. Monitoring the data lake

Beyond cost control and error detection, continuous monitoring of the implemented data lake is an important preventive measure for the timely detection of potential data bottlenecks. This allows for timely query optimization and informed resource allocation, optimizing both performance and infrastructure costs.

Common Pitfalls to Avoid

1. Technology-centered design approach

Data experts in charge of the data lake implementation project sometimes tend to lose sight of business objectives and use cases and lean towards using unnecessary or inadequate technologies and tools to implement a data lake. It is important to remember that the project is only successful if it fulfills its requirements and brings business value to stakeholders, and the technological aspect only assumes a supportive role along the way.

2. Inadequate data management and architecture

An unclear understanding of source data ingested into the data lake is often the main reason behind the misalignments between the actual characteristics of collected data sets and established procedures for their management, leading to suboptimal data lake performance and resource utilization.

3. Finding the balance in data governance

A data lake tends to quickly become an unusable data swamp, draining the organization’s resources without properly employed governance mechanisms. Establishing a data catalog of data assets and a proper data security strategy facilitates continuous monitoring and validation of data quality and access. However, putting too much attention on data governance can significantly decrease the availability of data due to strict data access policies and, ultimately, reduce its value to the data lake.

4. Poor data lake design – storage and computation costs

A common data lake design approach is establishing multiple layers (ingestion, storage, processing, and consumption) to implement different stages of the data pipeline.  The resulting data lake’s poor performance is often due to the poor design of those layers, whereby organizations assign insufficient and suboptimal storage and compute resources to handle data workloads within a layer and the overall data lake.

Conclusion 

The success of the data lake project is heavily linked to the quality of the design process preceding its implementation. Prioritizing data governance and quality, ensuring robust security measures for ensuring controlled data access and compliance with data regulations, and optimizing data access with optimizations in data management tasks are crucial for maximizing its value.

However, pitfalls such as insufficient planning and understanding of the expected business outcome, inadequate data management, and governance mechanisms can hinder data lake success. By carefully considering these best practices and proactively addressing potential challenges, organizations can achieve the true potential of their data lakes. To migrate your data to a data lake such as Amazon S3, try Hevo. Sign up for a 14-day free trial and experience seamless data migration.

Frequently Asked Questions