AWS vs Azure vs Google Cloud: Data Lake Technology Comparison

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Today, businesses’ success is determined by their ability to derive value from their data. Aside from the promise of a competitive advantage over their peers, organizations often implement data lakes to capitalize on advanced analytical capabilities or to modernize traditional approaches such as data access and speed of retrieval.

For managed service providers (MSPs) and others providing data services, it’s inevitable that your customers’ big data analytics efforts will eventually include data lake technology to get the most insight from their data. That could result in an opportunity for MSPs, specifically via cloud data lake platforms.

The popularity of data lakes continues to rise as customers need data storage and analytics solutions that are more flexible and agile than legacy data management systems. As the competition between the top cloud providers ramps up, Amazon, Microsoft, and Google each provide formidable data lake technologies and solutions.

Also read: How Can the Channel Use AI and ML?

What Is Data Lake on AWS?

Data Lake on AWS is data lake technology that offers organizations the option to manage and store diverse types of data from different sources.

Through the AWS Cloud, customers enjoy numerous building blocks for the application of flexible, secure, and cost-effective data lakes as well as support from AWS.

Data Lake on AWS deploys a cost-effective data lake architecture on AWS Cloud that offers high availability and a user-friendly console to search and request datasets. It automatically configures core AWS services required to conveniently tag, search, share, analyze, transform, and govern particular data subsets across an organization or with external users.

Data Lake on AWS Key Differentiators

  • Data Access Flexibility: Data Lake on AWS enables customers to leverage pre-signed Amazon S3 URLs or use a suitable AWS identity and access management (IAM) for controlled but direct access to Amazon S3 datasets.
  • Federation Sign-In: Customers can allow users to sign in through a Security Assertion Markup Language (SAML) provider like Microsoft Active Directory Federation Services.
  • Managed Storage Layer: Through a managed Amazon S3 bucket, Data Lake on AWS customers can manage and secure data storage and retrieval. They can also use solution-specific AWS Key Management Service (KMS) keys for encryption of data at rest.
  • User Interface: Data Lake on AWS’s user interface has an intuitive web-based console delivered by Amazon CloudFront and hosted on Amazon S3. Through the console, customers can manage data lake users, packages, and policies, as well as design manifests of datasets.
  • Command-Line Interface: The provided command-line interface (CLI) or API can be easily used to automate data lake tasks.

Pricing: Depending on the services you need, AWS provides a price calculator to help you generate an estimate. You can also contact their sales team for a custom quote.

Also read: Databricks Helps Partners Tap Its Lakehouse Platform for Data and AI Services

What Is Azure Data Lake?

Azure Data Lake is a Microsoft product that features all the functionality developers, analysts, and data scientists need to simplify all types of data storage, processing, and analytics across languages and platforms.

With Azure Data Lake, the complexities involved in importing and storing data of all shapes, sizes, and speeds are eliminated for customers. It also simplifies the use of batch, streaming, and interactive analysis.

Customers can also use Azure Data Lake with existing IT security, identity, and management investments to enjoy much simpler data governance and management. Furthermore, users can extend their current applications with Azure Data Lake, as it smoothly integrates with data warehouses and operational stores.

As a service capable of meeting customers’ current and future business needs, Azure Data Lake eliminates several scalability and productivity issues that prevent customers from optimizing the value of their data assets.

Azure Data Lake Key Differentiators

  • Data Lake Analytics: Data Lake Analytics is one of the tools Azure provides to build your data lake solutions. It removes the limits from data lake analytics, allowing customers to effortlessly build and execute parallel data transformation and processing programs over petabytes of data. Data Lake Analytics also allows users to pay per job, plus scale and process data on demand, as there is no infrastructure to manage.
  • HDInsight: HDInsight users have a fully managed Cloud Hadoop offering that delivers maximized open-source analytic clusters for multiple big data technologies. These technologies include Hive, Map Reduce, HBase, Spark, Kafka, and more. HDInsight allows customers to deploy these as managed clusters while providing enterprise-grade monitoring and security.
  • Integration With Existing IT Investments: Azure Data Lake eliminates the challenges of integrating big data with existing IT investments. It works with Power BI, Azure Synapse Analytics, Data Factory, Azure SQL Server, Azure SQL Database, and more. Azure Data Lake can connect to application-generated data or data ingested by devices in IoT (Internet of Things) environments.
  • Data Lake Storage and Analysis of Petabyte-Size Files: Azure Data Lake is not only secure but also highly scalable and built to the open HDFS standard. Organizations can analyze all of their data in one place without artificial constraints. Data Lake Storage is designed to store trillions of files, and a single file can exceed a petabyte in size.

Pricing: The cost is based on terabytes per month and is largely determined by data storage, capacity reservations, transaction, and more. See Azure Data Lake’s pricing page for full cost information.

What Is Google Cloud Platform?

Google Cloud Platform (GCP) is a suite of cloud computing tools that approaches data lakes through autoscaling services that enable customers to create data lakes to integrate with their existing IT investments, applications, and technologies.

These autoscaling services include Dataflow, BigQuery, Cloud Data Fusion, Cloud Storage, and Dataproc. Data lake modernization, however, is Google Cloud’s data lake solution, which enables teams to ingest, store, and analyze massive volumes of heterogeneous, full-fidelity data in a secure and cost-effective manner.

Additionally, Google has a new product based on the BigQuery service called BigLake, which helps organizations unify their data warehouses and data lakes without worrying about compatibility across all sources. BigLake enables organizations to carry out standardized fine-grained access control and accelerate query performance across multicloud storage and open formats. It’s worth noting that Google terms BigLake as a data lakehouse, which is a combination of data lakes and data warehouses and includes machine learning, data management and optimization, and governance features.

Google Cloud Platform Key Differentiators

  • Fully Managed Services: Google’s data lake modernization solution offers customers autoscaling, provisioning, and governance capabilities for data and analytic open-source software clusters like Apache Spark for simpler management in a matter of minutes.
  • Integrated Data Science and Analytics: Customers can build, train, and deploy analytics quicker on a Google data lake with analytics accelerators such as BigQuery, Apache Spark, and GPUs (graphics processing units).
  • Cost Management: The autoscaling services provided by Google Cloud enable users to detach compute from storage to improve query speeds and manage cost at a per-GB level.
  • Multi-Compute Analytics: BigLake enables users to not only maintain a single copy of data but also make the data consistently available across Google Cloud and open-source engines.
  • Performance Acceleration: Customers can reach best-in-class performance over data lake tables on Google Cloud, Azure, and AWS on BigLake by proven BigQuery infrastructure.

Pricing: Google invites prospective customers to contact them for quotes and other pricing information regarding the Google Cloud product combinations that interest them.

AWS vs Azure vs Google Cloud Comparison

Below is a comparison table for Amazon, Microsoft, and Google’s data lake products:

Comparison table for AWS vs Azure vs Google Cloud

The best product for your business will depend largely on your unique needs.

Choosing a Data Lake Solution

To choose the correct data lake solution for your organization, consider which platform provides the best balance between your desired performance and budget to ensure that your teams aren’t overrun when your analytics needs grow. It’s important to determine whether to employ managed analytics services or manage your own data lake, which is dependent on your resources and analytics needs. 

You should also consider a data lake solution that offers you the flexibility to satisfy as many of your use cases as possible, shift your workloads to the cloud, and help you avoid data silos. Finally, remember that alignment between IT and business is key to a successful data lake initiative.

Collins Ayuya
Collins Ayuya
Collins is a contributing writer for Channel Insider and lives in Nairobi, Kenya. His tech and channel articles reflect his specialties in AI, cybersecurity, cloud computing, embedded systems, and the Internet of things (IoT). Collins has a bachelor’s degree in telecommunications and IT and is currently earning his master’s in computer science. He also has a particular interest in the startup world, having worked as a head of product overseeing sales and marketing teams and later founding his own tech startup. In his free time, Collins enjoys playing soccer and video games, drawing, and traveling.


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