Amazon EMR vs Databricks

Overview

Amazon EMR

Stacks543

Followers682

Votes54

Databricks

Stacks525

Followers768

Votes8

Amazon EMR vs Databricks: What are the differences?

Introduction

In this article, we will explore the key differences between Amazon EMR and Databricks. Both Amazon EMR and Databricks are popular big data processing platforms, but they have some distinct features and capabilities that differentiate them. Understanding these differences can help in making an informed choice when selecting a platform for your big data processing needs.

Scalability and Managed Services: Amazon EMR is a fully managed cluster platform that allows you to easily provision, scale, and manage a cluster of compute resources for big data processing. It provides automatic scaling capabilities to handle variable workloads efficiently. Databricks, on the other hand, is a unified analytics platform that offers a managed service for big data and machine learning. It provides auto-scaling clusters and a serverless architecture for optimized resource utilization.
Integration with Cloud Services: Amazon EMR integrates seamlessly with other AWS services, such as Amazon S3 for data storage, Amazon Redshift for data warehousing, and Amazon Kinesis for real-time streaming data processing. It leverages the full range of AWS services to build end-to-end big data pipelines. Databricks also offers integrations with various cloud services, including Azure Blob Storage, Azure Data Lake Storage, and Azure Event Hub. It is tightly integrated with the Azure ecosystem and provides native integration with Azure services.
Notebook Environment: Databricks provides a collaborative notebook environment that allows data scientists and engineers to create and execute code, visualize data, and share insights. It offers built-in support for popular programming languages like Python, R, and Scala. Amazon EMR, on the other hand, doesn't have a native notebook environment. It supports popular open-source tools like Apache Zeppelin, Jupyter Notebook, and RStudio, which can be installed on the cluster for interactive data analysis.
Machine Learning Capabilities: Databricks has strong native integration with popular machine learning frameworks like Apache Spark MLlib and TensorFlow. It provides a comprehensive machine learning library and tools for building and deploying machine learning models at scale. Amazon EMR also supports machine learning frameworks like Apache Spark and TensorFlow, but it doesn't have the same level of native integration and built-in tools as Databricks.
Enterprise Features and Security: Databricks offers advanced enterprise features like fine-grained access controls, data encryption at rest and in transit, and integration with Active Directory for user authentication. It provides a robust security infrastructure to ensure data privacy and compliance. Amazon EMR also provides enterprise features like encryption, fine-grained access controls, and integration with AWS Identity and Access Management (IAM). However, Databricks offers a more comprehensive set of security features tailored specifically for big data processing.
Pricing and Cost Management: Amazon EMR offers a flexible pricing model based on the EC2 instances and storage resources used by the cluster. It provides options for on-demand instances, reserved instances, and spot instances to optimize cost. Databricks pricing is based on the DataBricks Units (DBUs), which combines compute and storage resources. It offers different pricing tiers based on the usage patterns and requirements. Databricks provides cost management tools and optimization recommendations to help reduce overall costs.

In summary, Amazon EMR is a fully managed big data processing platform with strong integration with AWS services, while Databricks is a unified analytics platform with a focus on collaboration and machine learning. Databricks provides a richer set of native tools and capabilities, but Amazon EMR offers more flexibility and integration options with the AWS ecosystem. Ultimately, the choice between Amazon EMR and Databricks depends on specific requirements, skillsets, and preferences for cloud platform providers.

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Detailed Comparison

Amazon EMR	Databricks
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.	Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster;Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.;Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.;Hadoop Tools- EMR supports powerful and proven Hadoop tools such as Hive, Pig, and HBase.	Built on Apache Spark and optimized for performance; Reliable and Performant Data Lakes; Interactive Data Science and Collaboration; Data Pipelines and Workflow Automation; End-to-End Data Security and Compliance; Compatible with Common Tools in the Ecosystem; Unparalled Support by the Leading Committers of Apache Spark
Statistics
Stacks 543	Stacks 525
Followers 682	Followers 768
Votes 54	Votes 8
Pros & Cons
Pros 15 On demand processing power 12 Don't need to maintain Hadoop Cluster yourself 7 Hadoop Tools 6 Elastic 4 Backed by Amazon	Pros 1 Multicloud 1 Data stays in your cloud account 1 Security 1 Usage Based Billing 1 Databricks doesn't get access to your data
Integrations
No integrations available	MLflow Delta Lake Kafka Apache Spark TensorFlow Hadoop PyTorch Keras

What are some alternatives to Amazon EMR, Databricks?

Google Analytics

Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.

Mixpanel

Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience.

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Piwik

Matomo (formerly Piwik) is a full-featured PHP MySQL software program that you download and install on your own webserver. At the end of the five-minute installation process, you will be given a JavaScript code.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Clicky

Clicky Web Analytics gives bloggers and smaller web sites a more personal understanding of their visitors. Clicky has various features that helps stand it apart from the competition specifically Spy and RSS feeds that allow web site owners to get live information about their visitors.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Related Comparisons

Amazon EMR vs Databricks: What are the differences?

Introduction

Scalability and Managed Services: Amazon EMR is a fully managed cluster platform that allows you to easily provision, scale, and manage a cluster of compute resources for big data processing. It provides automatic scaling capabilities to handle variable workloads efficiently. Databricks, on the other hand, is a unified analytics platform that offers a managed service for big data and machine learning. It provides auto-scaling clusters and a serverless architecture for optimized resource utilization.
Integration with Cloud Services: Amazon EMR integrates seamlessly with other AWS services, such as Amazon S3 for data storage, Amazon Redshift for data warehousing, and Amazon Kinesis for real-time streaming data processing. It leverages the full range of AWS services to build end-to-end big data pipelines. Databricks also offers integrations with various cloud services, including Azure Blob Storage, Azure Data Lake Storage, and Azure Event Hub. It is tightly integrated with the Azure ecosystem and provides native integration with Azure services.
Notebook Environment: Databricks provides a collaborative notebook environment that allows data scientists and engineers to create and execute code, visualize data, and share insights. It offers built-in support for popular programming languages like Python, R, and Scala. Amazon EMR, on the other hand, doesn't have a native notebook environment. It supports popular open-source tools like Apache Zeppelin, Jupyter Notebook, and RStudio, which can be installed on the cluster for interactive data analysis.
Machine Learning Capabilities: Databricks has strong native integration with popular machine learning frameworks like Apache Spark MLlib and TensorFlow. It provides a comprehensive machine learning library and tools for building and deploying machine learning models at scale. Amazon EMR also supports machine learning frameworks like Apache Spark and TensorFlow, but it doesn't have the same level of native integration and built-in tools as Databricks.
Enterprise Features and Security: Databricks offers advanced enterprise features like fine-grained access controls, data encryption at rest and in transit, and integration with Active Directory for user authentication. It provides a robust security infrastructure to ensure data privacy and compliance. Amazon EMR also provides enterprise features like encryption, fine-grained access controls, and integration with AWS Identity and Access Management (IAM). However, Databricks offers a more comprehensive set of security features tailored specifically for big data processing.
Pricing and Cost Management: Amazon EMR offers a flexible pricing model based on the EC2 instances and storage resources used by the cluster. It provides options for on-demand instances, reserved instances, and spot instances to optimize cost. Databricks pricing is based on the DataBricks Units (DBUs), which combines compute and storage resources. It offers different pricing tiers based on the usage patterns and requirements. Databricks provides cost management tools and optimization recommendations to help reduce overall costs.

Amazon EMR vs Databricks

Overview