Top Data Lake Solutions for 2022

Data lakes have become a critical solution for enterprises to store and analyze data.

A cloud data lake solution offers a number of benefits that make it an ideal tool for managing and processing data, including protection of sensitive information, scalability of storage and resources, and automation of data-related processes. We’ll look at the top cloud data lake solutions available in the market and offer some insight into their key features, use cases and pricing.

Benefits of Data Lake Solutions

A data lake provides businesses with a robust data store perfect for pooling various data types, whether structured or unstructured. Data lakes also provide organizations with an optimal system for processing and analyzing their information.

Companies can easily set up pipelines to extract data from one storage area in the lake to another, which means they don’t have to worry about different platforms getting in the way of accessing the same content. A data lake solution can include all kinds of analytics tools, including natural language processing (NLP), artificial intelligence and machine learning (AI/ML), text mining, and predictive analytics to offer real-time insights into customer needs and business trends.

The cloud-based platform offers incredible scalability, allowing companies to grow as their data grows without interruption in services. With data lakes, it’s possible to analyze what works and doesn’t work within an organization at lightning speed.

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Common Features of Data Lake Solutions

Data lake solutions have many features in common, such as data visualization, data access and sharing, scalability, and so on. Here are some common characteristics of data lake solutions.

• Data visualization enables users to explore and analyze large volumes of unstructured data by creating interactive visualizations for insights into their content.
• Scalability allows companies with both small and large databases to handle sudden spikes in demand without worrying about system failure or crashes due to a lack of processing power.
• File upload/download enables uploading and downloading files from the cloud or local servers into the data lake area.
• Machine learning helps AI systems learn about different types of information and detect patterns automatically.
• Integration facilitates compatibility across multiple software programs; this makes it easier for organizations to use whichever application they choose without having to worry about incompatibility issues between them.
• Data accessibility ensures that any authorized user can access the necessary files without waiting for lengthy downloads or parsing times.

The Best Cloud Data Lake Solutions

Here are our picks for the best data lake solutions based on our analysis of the market.

Snowflake

Snowflake is a SaaS (software-as-a-service) company that provides businesses an all-in-one single platform for data lakes, data warehousing, data engineering, data science and machine learning, data application, collaboration, and cybersecurity. The Snowflake platform breaks down barriers between databases, processing systems, and warehouses by unifying them into a single system to support an enterprise’s overall data strategy.

With Snowflake, companies can combine structured, semi-structured, and unstructured data of any format, even from across clouds and regions, as well as data generated from Internet of Things (IoT) devices, sensors, and web/log data.

Key Differentiators

• Consolidates Data: Snowflake can be used to store structured, semi-structured, and unstructured data of any format, no matter where it originates or how it was created.
• Unified Storage: Snowflake combines many different types of data management functions, including storage and retrieval, ETL workflows, security management, monitoring, and analytics.
• Analyze With Ease: The unified design lets users analyze vast amounts of diverse datasets with extreme ease and speed.
• Speed up AI Projects: Snowflake offers enterprise-grade performance without requiring extensive resources or time spent on complex configurations. Additionally, with integrated GPU and parallel computing capabilities, analyzing large datasets is faster.
• Data Query: Analysts can query data directly over the data lake with good scalability and no resource contention or concurrency issues.
• Governance and Security: All users can access data simultaneously without performance degradation, ensuring compliance with IT governance and privacy policies.

Cost

Snowflake does not list pricing details on their website. However, prospective buyers can join their weekly product demo or sign up for a 30-day free trial to see what this solution offers.

Databricks

Databricks is a cloud-based data platform that helps users prepare, manage, and analyze their data. It offers a unified platform for data science, engineering, and business users to collaborate on data projects. The application also integrates with Apache Spark and AWS Lambda, allowing data engineers to build scalable batch or streaming applications.

Databricks’s delta lake provides a robust transactional storage layer that enables fast reads and writes for ad hoc queries and other modern analytical workloads. Delta lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

Key Differentiators

• Databricks can cluster resources across multiple clusters to provide scale and fault tolerance.
• ​​The Databricks data lakehouse combines data warehouses and data lakes into a single platform that can manage all the corporate data, analytics, and AI use cases.
• The platform is built on open source.
• Databricks provides excellent performance with Apache Spark.
• The platform provides a unified source of information for all data, including real-time streams, ensuring high-quality and reliable data.

Costs

Databricks offers pay-as-you-go pricing. However, starting prices vary based on the cloud provider. A 14-day free trial is available for users that want to try it before buying.

Also read: Snowflake vs. Databricks: Big Data Platform Comparison

Cloudera data lake service

Cloudera data lake service is a cloud-based big data processing platform that helps organizations effectively manage, process, and analyze large amounts of data. The platform is designed to handle structured and unstructured data, making it ideal for a wide range of workloads such as ETL, data warehousing, machine learning, and streaming analytics.

Cloudera also provides a managed service called Cloudera Data Platform (CDP), which makes it easy to deploy and manage data lakes in the cloud. It is one of the top cloud data lake solutions because it offers numerous features and services.

Key Differentiators

• CDP can scale to petabytes of data and thousands of diverse users.
• Cloudera governance and data log features transform metadata into information assets, increasing its usability, reliability, and value throughout its life cycle.
• Data can be encrypted at rest and in motion, and users are enabled to manage encryption keys.
• Cloudera Data Lake Service defines and enforces granular, flexible, role- and attribute-based security rules as well as prevents and audits unauthorized access to classified or restricted data.
• The platform provides single sign-on (SSO) access to end users via Apache Knox’s secure access gateway.

Cost

Cloudera data lake service costs $650 per Cloudera Compute Unit (CCU) per year. Prospective buyers can contact the Cloudera sales team for quotes tailored to their needs.

Amazon web service lake formation

Amazon Web Services (AWS) Lake Formation is a fully managed service that makes it easy to set up a data lake and securely store and analyze data. With Lake Formation, users can quickly create a data lake, ingest data from various sources, and run analytics on data using the tools and services of their choice. Plus, Lake Formation provides built-in security and governance features to help organizations meet compliance requirements. Amazon Web Services also offers Elastic MapReduce, a hosted service that lets users access their cluster without having to deal with provisioning hardware or complex setup tasks.

Key Differentiators

• Lake formation cleans and prepares data for analysis using an ML transform called FindMatches.
• Lake formation enables users to import data from various database engines hosted by AWS. The supported database engines include MySQL, PostgreSQL, SQL Server, MariaDB, and Oracle database.
• Users can also use the AWS SDKs (software development kits) or load files into S3 and then use AWS Glue or another ETL tool to move them into lake formation.
• Lake Formation lets users filter data by columns and rows.
• The platform has the capabilities to rewrite various date formats for consistency and also make data analytically friendly.

Cost

AWS pricing varies based on region and number of bytes scanned by the storage API, rounded to the next megabyte, with a 10MB minimum. AWS charges for data filtering ($2.25 per TB of data scanned), transaction metadata storage ($1.00 per 100,000 S3 objects per month), request ($1.00 per million requests per month), and storage optimizer ($2.25 per TB of data processed). Companies can use the AWS pricing calculator to get an estimate or contact an AWS specialist for a personalized quote.

Azure data lake

Azure Data Lake is Microsoft’s cloud-based data storage solution that allows users to capture data of any size, type, and ingestion speed. Azure Data Lake integrates with enterprise IT investments for identity, management, and security. Users can also store any kind of data in the data lake, including structured and unstructured datasets, without transforming it into a predefined schema or structure.

Key Differentiators

• YARN (yet another resource negotiator) enables Azure Data Lake to offer elasticity and scale, so the data can be accessed when needed.
• Azure Data Lake provides encryption capabilities at rest and in transit and also has other security capabilities, including SSO, multi-factor authentication (MFA), and management of identities built-in through Azure Active Directory.
• Analyzing large amounts of data from diverse sources is no longer an issue. Azure Data Lake uses HDInsight, which includes HBase, Microsoft R Server, Apache Spark, and more.
• Azure Data Lake allows users to quickly design and execute parallel data transformation and processing programs in U-SQL, R, Python, and .Net over petabytes of data.
• Azure HDInsight can be integrated with Azure Active Directory for role-based access controls and single sign-on.

Cost

Prospective buyers can contact the Microsoft sales team for personalized quotes based on their unique needs.

Google BigLake

Google BigLake is a cloud-based storage engine that unifies data lakes and warehouses. It allows users to store and analyze data of any size, type, or format. The platform is scalable and easily integrated with other Google products and services. BigLake also features several security and governance controls to help ensure data quality and compliance.

Key Differentiators

• BigLake is built on open format and supports major open data formats, including Parquet, Avro, ORC, CSV, and JSON.
• It supports multicloud governance, allowing users to access BigLake tables as well as those created in other clouds such as Amazon S3 and Azure Data Lake Gen 2 in the data catalog.
• Using BigLake connectors, users can keep a single copy of their data and make it available in the same form across Google Cloud and open-source engines like BigQuery, Vertex AI, Spark, Presto, Trino, and Hive.

Cost

BigLake pricing is based on BigLake table queries, which include BigQuery, BigQuery Omni, and BigQuery Storage API.

Hadoop

Apache Hadoop is an open-source framework for storing and processing big data. It is designed to provide a reliable and scalable environment for applications that need to process vast amounts of data quickly. IBM, Cloudera, and Hortonworks are some of the top providers of Hadoop-based software. 

Key Differentiators

• Hadoop data lake architecture is made of several modules, including HDFS (Hadoop distributed file system), YARN, MapReduce, and Hadoop common.
• Hadoop stores various data types, including JSON objects, log files, images, and web posts.
• Hadoop enables the concurrent processing of data. This is because when data is ingested, it is segmented and distributed across various nodes in a cluster.
• Users can gather data from several sources and act as a relay station for data that is overloading another system.

Cost

Hadoop is an open-source solution, and it’s available for enterprises to download and use at no cost.

Choosing a Data Lake Provider

There are various options for storing, accessing, analyzing, and visualizing enterprise data in the cloud. However, every company’s needs are different. The solution that works best for a company will depend on what they need to do with their data, where it lives, and what business challenges they’re trying to solve.

There are many factors to consider when choosing a data lake provider. Some of the most important include:

• Security and Compliance: Ensure the provider meets security and compliance needs.
• Scalability: Businesses should choose a provider they can scale with as their data needs grow.
• Cost: Compare pricing between providers to find the most cost-effective option.
• Ease of Use: Consider how easy it is to use the provider’s platform and tools.

Read next: Top Big Data Storage Products

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