ClickHouse Cloud VS Amazon EMR

Compare ClickHouse Cloud VS Amazon EMR and see what are their differences

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ClickHouse Cloud

Serverless Hosted ClickHouse Offering. Cloud-agnostic, No infrastructure to manage, Automatic scaling, Consumption-based pricing, Turnkey data migration services, World-class security and privacy guarantees.

Amazon EMR

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

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2023-04-02

ClickHouse Cloud

Website: clickhouse.com

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Amazon EMR

Website: aws.amazon.com

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ClickHouse Cloud features and specs

No features have been listed yet.

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.

Analysis of Amazon EMR

Overall verdict

Yes, Amazon EMR is generally considered a good option for organizations that need to handle large-scale data processing and analysis. Its integration with the AWS ecosystem, flexibility in resource management, and support for a wide array of big data frameworks make it a strong contender in the cloud-based big data processing market.

Why this product is good

Amazon EMR (Elastic MapReduce) is a robust cloud service provided by AWS for processing and analyzing large datasets quickly and cost-effectively. It simplifies running big data frameworks like Apache Hadoop and Apache Spark on AWS, offering scalability, flexibility, and integration with other AWS services. EMR is favored for its ability to dynamically allocate resources, thus optimizing both performance and cost for big data processing needs.

Recommended for

Amazon EMR is recommended for data engineers, data scientists, and IT professionals who need to manage and process large datasets in a scalable, efficient, and cost-effective manner. It is especially suitable for businesses that are already using AWS services and want to leverage a tightly integrated ecosystem. Additionally, it is a good choice for organizations that require rapid and flexible data analysis capabilities provided by frameworks such as Hadoop, Spark, HBase, and Presto.

ClickHouse Cloud videos

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Amazon EMR videos

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Amazon EMR Masterclass

Category Popularity

0-100% (relative to ClickHouse Cloud and Amazon EMR)

ClickHouse Cloud

Amazon EMR

Big Data As A Service

100 100%

Big Data As A Service

0% 0

Data Dashboard

0 0%

Data Dashboard

100% 100

Big Data

5 5%

Big Data

95% 95

Data Integration

100 100%

Data Integration

0% 0

User comments

Share your experience with using ClickHouse Cloud and Amazon EMR. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Amazon EMR should be more popular than ClickHouse Cloud. 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.

ClickHouse Cloud mentions (2)

Show HN: We built a ClickHouse-based logging service
> Note: hosted CH did not exist yet. We are using Altinity to run our cluster. It exists now actually. We (highlight) are on hosted clickhouse, which went in GA a few months ago. https://clickhouse.com/cloud. - Source: Hacker News / about 2 years ago
How ClickHouse helps us track billions of GraphQL requests monthly
After looking for alternatives we decided to switch to ClickHouse. We highly recommend reading the "100x Faster: GraphQL Hive migration from Elasticsearch to ClickHouse" article we wrote with the ClickHouse Cloud team. It covers in detail the problems we were facing and the decision-making process that lead us to love ClickHouse. - Source: dev.to / over 2 years ago

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: about 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