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Top Data Lake Tools in 2021

The Top 4 Data Lake Tools for 2021

Data Lakes – The term unofficially means the freedom to store all data in their raw formats and structures. 

Data lakes save the cost as well when implemented in the cloud, all while avoiding the hardware management headache!

Hence, businesses are increasingly tapping into Data Lakes to uncover hidden insights and gain a competitive edge in the market.

In this blog post, we’ll take a look at the top 4 Data Lake tools of 2021.

Here’s the list:

  1. Azure Data Lake Storage
  2. Amazon S3
  3. Qubole
  4. Snowflake

Let’s now take a look at each of them.

  1. Azure Data Lake Storage

Microsoft’s offering for the Data Lake needs is the Azure Data Lake Storage

The Azure Data Lake Storage is a single platform for the end-to-end management of a Data Lake, from Data ingestion to Data storage to analytics capabilities, and everything in between!

Azure Data Lake Storage Gen 2 combines the capabilities of Gen 1 with the Azure blob storage. It’s massively scalable and is able to execute queries on a large scale without compromising on performance.

In terms of the directory, the Azure Data Lakes are flexible with both flat and hierarchical namespace. From a security point of view, the Azure Data Lakes come with Azure Active Directory (AD) and role-based Access Control (RBAC).

As a Microsoft product, the Azure Data Lakes easily integrates with Power BI.

From a pricing standpoint, the cost is based on the data stored and processed, like for any other Azure product. Microsoft provides users with the necessary cost control mechanisms. The users can set up automated lifecycle management policies to optimize storage costs.

The Azure Data Lake Storage complies with a comprehensive list of regulations including HIPAA, ISO, IRS, and many more.

  1. Amazon S3

The S3 in “Amazon S3” is the short form of Simple Storage Service.

From a technical standpoint, it’s an object-based storage service, where you can store highly unstructured data including images, videos, and audios. 

The object-based storage service makes it easy to store or retrieve the data as all data is stored in a single bucket (in a flat directory). Amazon supports folder-based directories, just for the end-user convenience. But behind the scene, the objects are stored as folderName/fileName.fileExtension format. 

Ex. albums/album1/song2.mp4 is an object, where ‘albums’ is the console (or folder) name, which contains a list of albums. Album1 is the name of the specific album, which again is a console, containing the individual songs such as song2.mp4.

Amazon S3 data lakes are highly suitable for storing unstructured data as they are less likely to change with time.

In order to process and analyze such highly unstructured data stored in an S3 data lake, Amazon provides the users with in-built machine learning integration options, especially its own service called Amazon SageMaker. The users can build, train, and deploy ML models and derive insights out of the huge pile of unstructured data.

S3 offers unified data access, security, and governance, which helps to quickly comply with the critical industry-specific, and/or geographical regulatory requirements.

The AWS lake formation enables organizations to get started with secure S3 data lakes in a few days. Amazon S3 data lakes are easily scalable and come with flexible pricing plans.

  1. Qubole

Qubole Data Lake offers slightly different advantages over the rest, especially due to the integrations capabilities it offers for the third-party tools.

Qubole supports data ingestion with tools such as Talend, Informatica etc.

The Qubole Airflow as a service helps in authoring and monitoring data pipelines, while the Qubole Scheduler helps to automate data preparation and ingestion.

The Qubole pipeline service is a code generation wizard which helps build data streams without writing code for a vast list of sources and targets such as Kafka, S3, Snowflake, ElasticSearch, etc.

Qubole has a built-in connector for the leading data viz. Tools. Integrates with easily Looker and Tableau for ad-hoc analytics. It also supports ODBC/JDBC-based connectivity.

Qubole offers user-friendly Machine Learning (ML) features. It also offers the flexibility to scale up or scale down the compute capacity. So whether you want to meet the demand or cut costs, both are possible! Hence, it is easy to deploy enterprise-wide ML solutions. Qubole Integrates with Amazon SageMaker as well.

  1. Snowflake

Snowflake, though primarily offers cloud data warehouses, also comes with data lake capabilities. It’s built on top of the 3 cloud giant solutions – namely Azure, AWS, and Google Cloud.

In Snowflake, the data ingestion and transformation is achieved with the inbuilt tool – Snowpipe You can make use of streams and tasks for Change Data Capture (CDC).

Snowflake offers a data validation tool, Snowsight which also comes with limited data visualization capabilities. Snowsight helps organizations improve data quality.

In terms of performance, Snowflake offers Massively Parallel Processing (MPP) capabilities. You can also make use of Materialized views, and reuse the already executed queries instead of recreating them from scratch again.

In terms of regulatory compliances, Snowflake is compliant with critical regulations such as HIPAA, SOC1, SOC2, etc. Snowflake supports RBAC, MFA, SSO from a Data Security perspective.

From a pricing standpoint, Snowflake offers you the ability to save costs by setting up space optimization algorithms.

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