AWS vs. Azure vs. Google Cloud: Comparing Data Lake Technology

Businesses today are determined by their ability to extract value from their data. Aside from the promise of a competitive advantage over their rivals, companies often implement data lakes to take advantage of advanced analytics capabilities or to modernize traditional approaches like data access and retrieval speed.

For Managed Service Providers (MSPs) and other data service providers, it is inevitable that their customers’ big data analytics efforts will ultimately incorporate data lake technology to extract the greatest possible insights from their data. This could present an opportunity for MSPs, particularly via cloud data lake platforms.

Data lakes continue to grow in popularity as customers require data storage and analytics solutions that are more flexible and agile than traditional data management systems. As competition between the leading cloud providers increases, Amazon, Microsoft and Google each offer impressive data lake technologies and solutions.

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What is Data Lake on AWS?

Data Lake on AWS is a data lake technology that gives organizations the ability to manage and store different types of data from different sources.

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

Data Lake on AWS provides a cost-effective data lake architecture on the AWS Cloud that offers high availability and an easy-to-use console for searching and requesting datasets. It automatically configures core AWS services needed to conveniently tag, search, share, analyze, transform, and manage specific subsets of data within an organization or with external users.

Key Differentiators of Data Lake on AWS

  • Flexibility in data access: Data Lake on AWS allows customers to leverage pre-signed Amazon S3 URLs or use appropriate AWS identity and access management (IAM) for controlled but direct access to Amazon S3 records.
  • Federation Registration: Customers may allow users to log in through a Security Assertion Markup Language (SAML) provider such as Microsoft Active Directory Federation Services.
  • Managed storage tier: Through a managed Amazon S3 bucket, Data Lake on AWS customers can manage and secure data storage and retrieval. You can also use AWS Key Management Service (KMS) solution-specific keys to encrypt data at rest.
  • User interface: The Data Lake on AWS user interface features an intuitive web-based console provided by Amazon CloudFront and hosted on Amazon S3. Through the console, customers can manage Data Lake users, packages, and policies, as well as design dataset manifests.
  • 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 require, AWS provides a pricing calculator that you can use to generate an estimate. You can also contact the sales team for a custom quote.

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What is Azure Data Lake?

Azure Data Lake is a Microsoft product that includes all the capabilities developers, analysts, and data scientists need to simplify all types of data storage, processing, and analysis across languages ​​and platforms.

With Azure Data Lake, customers eliminate the complexities of importing and storing data of all shapes, sizes, and speeds. It also simplifies the use of batch, streaming, and interactive analytics.

Customers can also use Azure Data Lake with existing IT security, identity, and governance investments to benefit from much simpler data management and governance. Additionally, users can extend their current applications with Azure Data Lake as it seamlessly integrates with data warehouses and operational storage.

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 maximizing the value of their data assets.

Key differentiators of Azure Data Lake

  • Data Lake Analysis: Data Lake Analytics is one of the tools that Azure provides to build your data lake solutions. It breaks the boundaries of data lake analysis and enables customers to easily build and run parallel data transformation and processing programs on petabytes of data. Data Lake Analytics also allows users to pay per job, as well as scale and process data as needed, since there is no infrastructure to manage.
  • HDInsight: HDInsight users have a fully managed Cloud Hadoop offering that provides maximized open source analytics clusters across 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 into 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 data generated by applications or data ingested by devices in Internet of Things (IoT) environments.
  • Data lake storage and analysis of petabyte-sized files: Azure Data Lake is not only secure, but also highly scalable and built according to the open HDFS standard. Businesses can analyze all their data in one place without artificial limitations. Data Lake Storage is designed to store trillions of files, and a single file can exceed a petabyte in size.

Pricing: Cost is based on terabytes per month and is largely driven by data storage, capacity reservations, transactions, and more. For complete pricing information, see the Azure Data Lake pricing page.

What is Google Cloud Platform?

Google Cloud Platform (GCP) is a suite of cloud computing tools that addresses data lakes through autoscaling services that allow customers to build 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. However, data lake modernization is Google Cloud’s data lake solution that enables teams to securely and cost-effectively ingest, store, and analyze massive amounts of heterogeneous, high-fidelity data.

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

Key Differentiators of the Google Cloud Platform

  • Fully Managed Services: Google’s data lake modernization solution provides customers with autoscaling, provisioning, and governance capabilities for data and analytics open source software clusters like Apache Spark for easier management in minutes.
  • Integrated data science and analytics: Customers can build, train, and deploy analytics faster on a Google Data Lake with analytics accelerators such as BigQuery, Apache Spark, and GPUs (graphics processing units).
  • Cost management: Autoscaling services provided by Google Cloud allow users to separate processing power from storage to improve query speed and manage cost per GB.
  • Multi-Compute Analysis: BigLake not only allows users to maintain a single copy of data, but also makes the data available consistently across Google Cloud and open-source engines.
  • Performance Acceleration: Customers can achieve best-in-class performance across data lake tables in Google Cloud, Azure, and AWS on BigLake using proven BigQuery infrastructure.

Pricing: Google invites potential customers to contact them for quotes and other pricing information on the Google Cloud product combinations they are interested in.

Comparison between AWS, Azure and Google Cloud

Below is a comparison table for Amazon, Microsoft, and Google Data Lake products:

AWS vs Azure vs Google Cloud comparison table

The best product for your business largely depends on your unique needs.

Choosing a data lake solution

To select the right data lake solution for your business, consider which platform offers the best balance between your desired performance and budget to ensure your teams aren’t overwhelmed as your analytics needs grow. It’s important to decide whether to use managed analytics services or to manage your own data lake, depending on your resources and analytics needs.

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

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