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Amazon EMR VS Azure Data Lake Store

Compare Amazon EMR VS Azure Data Lake Store and see what are their differences

Amazon EMR logo Amazon EMR

Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Azure Data Lake Store logo Azure Data Lake Store

Azure Data Lake Storage Gen2 is highly scalable and secure storage for big data analytics. Maximize costs and efficiency through full integrations with other Azure products.
  • Amazon EMR Landing page
    Landing page //
    2023-04-02
  • Azure Data Lake Store Landing page
    Landing page //
    2023-03-17

Amazon EMR features and specs

  • Scalability
    Amazon EMR makes it easy to provision one, hundreds, or thousands of compute instances in minutes. You can easily scale your cluster up or down based on your needs.
  • Cost-effectiveness
    You only pay for what you use with EMR. There are no upfront fees. You can also leverage EC2 Spot Instances for a more cost-effective solution.
  • Ease of Use
    Amazon EMR has a user-friendly interface and integrates with a wide range of AWS services, making it easy to set up and manage big data frameworks like Apache Hadoop, Spark, etc.
  • Managed Service
    Amazon EMR takes care of the setup, configuration, and tuning of the big data environments, allowing you to focus on your data processing rather than managing infrastructure.
  • Security
    EMR integrates with AWS security features such as IAM for fine-grained access control, encryption options, and Virtual Private Cloud (VPC) for network security.
  • Flexibility
    Supports multiple big data frameworks including Hadoop, Spark, HBase, Presto, and more, facilitating a wide range of use cases.

Possible disadvantages of Amazon EMR

  • Complex Pricing Model
    EMR's pricing can be complex with costs varying based on instance types, storage, and data transfer. Predicting costs may be challenging.
  • Data Transfer Costs
    If your applications require transferring large amounts of data in and out of EMR, the associated costs can be significant.
  • Learning Curve
    Although EMR is easier to manage compared to on-premises solutions, there is still a learning curve associated with mastering the service and optimizing its various settings.
  • Vendor Lock-in
    Since EMR is an AWS service, you may find it difficult to migrate to another service or cloud provider without significant re-engineering.
  • Dependency on AWS Ecosystem
    The full potential of EMR is best realized when integrated with other AWS services. This can be limiting if your architecture uses services from multiple cloud providers.

Azure Data Lake Store features and specs

  • Scalability
    Azure Data Lake Storage is designed to handle massive amounts of data, scaling as your data requirements grow, which allows for the efficient processing and storage of large datasets.
  • Cost-effectiveness
    It offers a pay-as-you-go pricing model, which can be more cost-effective for businesses as it eliminates the need for large upfront investments in hardware.
  • Integration with Azure Ecosystem
    Azure Data Lake Storage integrates seamlessly with other Azure services, such as Azure Databricks and Azure Synapse, facilitating easier data analysis, processing, and management.
  • Security Features
    Robust security features include data encryption at rest and in transit, as well as fine-grained access controls, enabling enterprises to secure their data effectively.
  • Flexibility
    Supports a wide range of data types, from structured to semi-structured to unstructured, allowing for versatile data management and processing capabilities.

Possible disadvantages of Azure Data Lake Store

  • Complexity of Setup
    Initial setup and configuration can be complex and time-consuming, especially for users who are not familiar with Azure's environment and cloud-based storage solutions.
  • Learning Curve
    Users may experience a steep learning curve when first starting out with Azure Data Lake Storage, particularly those who do not have experience with Azure's cloud infrastructure.
  • Cost Management
    While pay-as-you-go pricing is advantageous, without proper monitoring and management, costs can quickly escalate with increasing data volumes and processing needs.
  • Performance Variability
    Performance may vary depending on the complexity and volume of data workloads, which might require additional tuning and optimization to meet performance requirements.
  • Dependency on Internet Connectivity
    Being a cloud service, it is highly dependent on internet connectivity, which can be a limitation for organizations in areas with unreliable internet access.

Amazon EMR videos

Amazon EMR Masterclass

More videos:

  • Review - Deep Dive into What’s New in Amazon EMR - AWS Online Tech Talks
  • Tutorial - How to use Apache Hive and DynamoDB using Amazon EMR

Azure Data Lake Store videos

No Azure Data Lake Store videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Amazon EMR and Azure Data Lake Store)
Data Dashboard
89 89%
11% 11
Data Warehousing
81 81%
19% 19
Big Data
89 89%
11% 11
Data Management
86 86%
14% 14

User comments

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Social recommendations and mentions

Based on our record, Amazon EMR should be more popular than Azure Data Lake Store. It has been mentiond 10 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Amazon EMR mentions (10)

  • 5 Best Practices For Data Integration To Boost ROI And Efficiency
    There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 2 years ago
  • What compute service i should use? Advice for a duck-tape kind of guy
    I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 3 years ago
  • Processing a large text file containing millions of records.
    This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 3 years ago
  • How to use Spark and Pandas to prepare big data
    Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 3 years ago
  • Beginner building a Hadoop cluster
    Check out https://aws.amazon.com/emr/. Source: about 3 years ago
View more

Azure Data Lake Store mentions (1)

  • Top 30 Microsoft Azure Services
    If you're deploying applications to the cloud, you'll need persistent data storage. Azure Blob Storage allows scalable storage for objects and files and provides an SDK to easily access them. Blob storage is a great trigger for Azure Functions, where uploading a file can automatically run your custom logic in the cloud (for example, if you wanted to run OCR on a file as soon as it's uploaded to a storage... - Source: dev.to / almost 4 years ago

What are some alternatives?

When comparing Amazon EMR and Azure Data Lake Store, you can also consider the following products

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

Snowflake - Snowflake is the only data platform built for the cloud for all your data & all your users. Learn more about our purpose-built SQL cloud data warehouse.

Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?