8 Data Lake Best Practices: Make the Most of Your Data Lake

Data Lake

What Are Data Lakes?

Data lakes are a storage mechanism for massive volumes of diverse data from various sources, enabling organizations to manage unstructured, semi-structured, and structured data at scale. Their applications span across multiple industries, providing a flexible, scalable solution for data storage and analysis.

For example, in healthcare, data lakes facilitate the storage and analysis of large datasets like patient records and clinical trial data, aiding in research and improving patient outcomes. Financial institutions use data lakes for real-time fraud detection and risk analysis by processing and analyzing transaction data. In the field of machine learning and artificial intelligence, data lakes house the extensive datasets necessary for training algorithms, enabling predictive analytics and advanced data modeling.

In this article:

  1. Use the Data Lake as a Landing Zone for Raw Data
  2. Secure the Data Lake with Access Controls
  3. Provide the Ability to Perform ACID Transactions on Data
  4. Use Metadata to Provide Context
  5. Make Multiple Copies of the Data
  6. Set Retention Policies
  7. Use a Data Catalog
  8. Monitor and Optimize Your Data Lake

The Importance of Data Lake Optimization

Optimizing data lakes is crucial for enhancing performance, managing storage costs, and ensuring data quality. An optimized data lake supports diverse analytics and machine learning, providing insights across various business functions. This leads to better decision-making and competitive advantage.

Due to the large scale and dynamic nature of data lakes, it’s important to keep them organized and well maintained. Otherwise, a data lake can turn into a data swamp, where the data becomes hard to extract and utilize. Data lakes are a powerful infrastructure, but they must be carefully designed, and require constant maintenance, to ensure they will be useful for their intended purpose.

Data Lake Best Practices

Here are some best practices to ensure that the data lake remains useful and secure.

1. Use the Data Lake as a Landing Zone for Raw Data

A data lake acts as a foundational platform and primary landing zone for raw data, enabling organizations to store large volumes of diverse data in its original form. This allows data to remain unstructured until specific needs dictate otherwise. The raw format preserves the full fidelity and potential of data, crucial for detailed and varied analytical processes.

Using a data lake as a landing zone also simplifies the integration and ingestion processes associated with new data sources. It provides a scalable architecture that can accommodate growing data volumes without compromising performance.

2. Secure the Data Lake with Access Controls

Use role and view-based access controls to protect the data in the lake. These controls restrict access to sensitive information based on the user’s role within the organization, ensuring that only authorized personnel can view or manipulate data. This layered security approach helps prevent unauthorized access and data breaches.

In addition to protecting data from external threats, role and view-based access controls facilitate a secure yet flexible working environment for data analysts and scientists. By clearly defining what data can be accessed by whom, organizations can streamline workflows and minimize the risk of accidental data exposure or misuse.

3. Provide the Ability to Perform ACID Transactions on Data

ACID transactions, which stand for Atomicity, Consistency, Isolation, and Durability, are not supported in data lakes by default due to the nature of their architecture. Data lakes are designed for scalability and handling vast amounts of data in various formats, which typically involves append-only storage and eventual consistency models. These characteristics make it challenging to implement traditional transactional features that ensure data is accurately updated and consistent at all times.

To enable ACID transactions in a data lake, organizations must integrate specialized tools or layers that offer transactional capabilities. One approach is to use transactional data lake frameworks such as Apache Hudi, Delta Lake, or Apache Iceberg. These frameworks modify how data is stored and accessed in the lake, introducing mechanisms like snapshot isolation, which allows handling concurrent reads and writes, and maintaining a consistent view of the data.

By implementing these technologies, a data lake can support ACID properties, making it possible to perform complex updates, deletes, and merge operations safely and efficiently. This capability is particularly important in environments where data integrity and correctness are critical, such as in financial reporting or inventory management systems.

4. Use Metadata to Provide Context

Metadata provides essential context to data, detailing its source, nature, and modifications, which helps users understand and utilize the data. This information is crucial for effective data management, as it supports functions like data discovery, quality assessment, and lineage tracking.

Well-managed metadata also helps ensure that data adheres to compliance standards and internal data governance policies, reducing risks associated with data mismanagement. Another advantage is that organizations can streamline the process of locating specific datasets, which in turn accelerates analysis and reduces the time spent on data preparation.

5. Make Multiple Copies of the Data

By duplicating data across different physical locations or storage systems, organizations can protect themselves against data loss due to hardware failures, natural disasters, or cyberattacks. This redundancy is particularly important for critical data that supports business operations, ensuring that no single point of failure can compromise data accessibility. Cloud-based storage services, and also advanced on-premises storage devices, enable redundancy by replicating data across multiple physical devices.

In addition to providing a fallback in case of emergencies, maintaining multiple copies of data allows organizations to distribute query loads across several servers, improving performance and reducing latency for data retrieval.

6. Set Retention Policies

A well-defined data retention policy is vital for managing the lifecycle of data within a data lake. Such policies dictate how long different types of data should be kept based on their relevance, legal requirements, and business value.

Implementing a clear retention strategy helps in avoiding data hoarding, which can lead to increased storage costs and management complexities. It also ensures compliance with industry regulations and legal standards, which often specify minimum or maximum periods for retaining certain types of data.

7. Use a Data Catalog

A data catalog serves as a centralized system where all data elements in the lake are indexed and described. It provides users with searchable metadata, including details about data origin, format, and usage, which facilitates effective data management and governance. By enabling better organization and discovery of data, a catalog makes it easier to find and use data.

A data catalog also supports stronger data governance by ensuring that all users have a common understanding of data definitions and structures. It promotes transparency and accountability in how data is modified and utilized across the organization.

8. Monitor and Optimize Your Data Lake

Continuous monitoring and optimization of a data lake are essential for maintaining its health and operational efficiency. Monitoring tools can provide real-time insights into data ingestion, storage, and access patterns, helping identify inefficiencies or anomalies that may affect performance.

Optimization also involves periodic reviews of data processing and storage technologies to ensure they remain aligned with the evolving demands of the business and advancements in technology. This might include implementing new data compression techniques, upgrading to faster storage solutions, or reorganizing data layouts to improve query performance.

Related content: Read our guide to data lake solutions (coming soon)

Data Lake with Cloudian

Powering Analytics with Secure On-Prem Data Lake 

Data analytics delivers insights, and the bigger the dataset, the more fruitful the analyses. However, storing massive amounts of data creates big challenges: cost, complexity, scalability, and data protection. To efficiently derive insight from information requires affordable, highly scalable storage that’s simple, reliable, and compatible with the tools you have.

Modernize your enterprise analytics infrastructure to a data lakehouse – the data analytics architecture of the cloud – by combining the flexibility, cost-efficiency, and scale of S3 data lakes with the data management and ACID transactions of data warehouses. Cloudian HyperStore provides a cost-effective, on-premises S3 data lake built on open standards that integrates seamlessly with the leading data warehouse platforms to bring the data lakehouse concept from the cloud to on-prem deployments for a true hybrid experience.

Cloud-Like Storage

Cloudian gives you capacity on demand, making it ideal for data lakes of semi-structured or unstructured data. To expand, simply add nodes, either at one site or across multiple sites. Manage it all within a single namespace, from a single management console, and search metadata across all your sites with a single query. Cloudian’s hybrid cloud capabilities even let you create and manage a data copy within AWS S3, if desired.

Learn more about data lakehouse with Cloudian.

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