There’s no question about the fact that organizations are drowning in data. As a result, business leaders are increasingly turning to data lakes – powerful solutions that can help harness the full potential of available data.

Nearly 50% of organizations benefit from data lakes and nearly one in three enterprises agree that a data lake is a central point for all accumulated data.

In this article, we’ll delve into the world of data lakes and explore the top data lake tools available – all with an eye to business security.

Understanding data lakes

What is a data lake?

A data lake is a centralized repository that stores, processes and secures large quantities of structured, semi-structured and unstructured data. A data lake can store data in its native format and process any variety of it – regardless of size limits.

A data lake enables enterprises to understand information at-speed. Whether the data comes from on-premises, cloud or edge computing systems, any type or volume of data can be processed in real-time (or in batch mode) and analyzed using SQL, Python, R, or any other language, third-party data or analytics application.

Why data lakes matter

Data lakes offer organizations several benefits that make them advantageous. For instance:

  • Scalability. Data lakes are highly scalable. They accommodate growth without requiring extensive infrastructure adjustments.
  • Flexibility. Data lakes support an array of data types; structured, semi-structured and unstructured.
  • Cost-efficiency. Retaining data in its raw form can be more cost-effective than traditional ET (extract, transform, load) processes.
  • Advanced analytics. Data lakes enable advanced analytics, machine learning and data exploration by offering access to a wide range of data.
  • Data democratization. With data lakes, data becomes accessible to an extensive internal audience, promoting data democratization.

Popular data lake tools

1. Azure Data Lake Storage. This tool aims to create a single unified storage space for data while keeping costs reasonable. Azure Data Lake Storage offers advanced security facilities, along with proper authentication of data via permissions management.

Key features include:

  • The ability to handle demanding workloads. Large-scale queries will not lead to compromised performance.
  • Storage costs are dependent upon storage usage.
  • Azure’s overall structure is known for having the maximum number of compliance certifications, meaning less worry for your organization.

2. AWS Lake Formation. AWS Lake Formation brands itself as one of the easiest data lake solutions to set up. Because AWS Lake Formation is housed under the larger umbrella of AWS, integration with AWS analytics and machine learning services is easy.

Key features include:

  • A searchable data catalog and a provision for custom-labeling, as per users’ convenience.
  • An audit log that assists users in identifying data access history across various services.
  • Integration with other analytics-based services, including Redshift, Athena or EMR.

3. Snowflake. Snowflake Inc. offers cloud-based data lakes using a new SQL database engine with unique cloud-based architecture. Launched in 2014, Snowflake’s data lake tool allows clients to store data in the cloud and to retain access to ready-to-use data analysis tools. 

Key points to know:

  • Snowflake ensures data security by encrypting data in-transit and at rest.
  • Snowflake also allows for secure data sharing and integration with third-party tools.

4. Google BigLake. This Google product, released in April of 2022, is a storage engine that allows organizations to analyze the data in their data warehouses and in data lakes. The tool leverages Google’s home-grown insights (based on its BigQuery data warehouse) and extends the insights to data lakes on Google Cloud Storage. Users can query the underlying data stores through a single system, without interruption.

Key points to know:

  • Google BigLake allows for fine-grained access control, eliminating the need to grant file level access to users.
  • It also offers multi-compute analytics, ensuring that a single copy of data is accessible across Google Cloud and open-source engines.

5. Qubole. Qubole can be conceptualized as an open data lake company, enhancing machine learning and analytical processing capabilities on data lakes.

What’s an open data lake? This particular data lake consists of data in an open format that can be accessed via open standards.

Key features include:

  • Ad-hoc analytics reports available through a single click.
  • Multiple streaming data pipelines that can be merged together and that can provide real-time unified insights.
  • Efficient data pipeline management.

Data lake vs. data warehouse

Although data lakes and data warehouses all store data on some level, each storage modality benefits different use-cases. As a point of comparison, data warehouses are often preferable for the type of repeatable reporting and analysis that’s common within business – monthly sales reports, tracking of sales within a specific region, web traffic…etc.

In theory, an enterprise may want both a data lake and a data warehouse.

Further thoughts

As organizations put their data to work for them, on-premise and cloud-based data lakes and data lake tools are likely to become business must-haves.

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