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Amazon EMR VS Apache Doris

Compare Amazon EMR VS Apache Doris and see what are their differences

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  3. » Reviews
  4. » Alternatives

Amazon EMR logo Amazon EMR

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

Apache Doris logo Apache Doris

Apache Doris is an open-source real-time data warehouse for big data analytics.
  • Amazon EMR Landing page
    Landing page //
    2023-04-02
  • Apache Doris Apache Doris
    Apache Doris //
    2024-01-10

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.

Apache Doris features and specs

  • High Performance
    Apache Doris is designed to deliver high query performance, especially for aggregate queries, due to its columnar storage and vectorized execution engine.
  • Real-time Analytics
    Supports real-time data analytics with low latency, thanks to its efficient data ingestion processes and real-time data update capabilities.
  • Unified Analytics
    Provides a unified platform that supports both real-time and batch data processing, offering flexibility for different analytical workloads.
  • Ease of Use
    Features a SQL-like interface, which makes it accessible for users familiar with SQL, reducing the learning curve.
  • Scalability
    Can scale out horizontally, allowing it to handle increasing volumes of data and user queries by adding more nodes to the cluster.

Possible disadvantages of Apache Doris

  • Ecosystem Integration
    While improving, the ecosystem isn't as mature as older database management systems, which might pose integration challenges with certain tools.
  • Community Support
    Being a relatively newer project, it may not have as large a community or as extensive third-party support as more established databases.
  • Complexity in Setup
    Initial setup and configuration can be complex, especially for users not already familiar with similar distributed systems.
  • Limited Use Cases
    Optimized specifically for online analytical processing (OLAP), it may not be suitable for all types of databases or transactional use cases.
  • Features Maturity
    Some features may lack the maturity and robustness found in more mature and widely adopted database systems, requiring careful evaluation based on project needs.

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.

Amazon EMR videos

+ Add

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

Apache Doris videos

No Apache Doris videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Amazon EMR and Apache Doris)

Amazon EMR

Apache Doris

Data Dashboard
100 100%
Data Dashboard
0% 0
Databases
0 0%
Databases
100% 100
Big Data
100 100%
Big Data
0% 0
Relational Databases
0 0%
Relational Databases
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Amazon EMR and Apache Doris

Amazon EMR Reviews

We have no reviews of Amazon EMR yet.
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Apache Doris Reviews

Log analysis: Elasticsearch vs Apache Doris
If you are looking for an efficient log analytic solution, Apache Doris is friendly to anyone equipped with SQL knowledge; if you find friction with the ELK stack, try Apache Doris provides better schema-free support, enables faster data writing and queries, and brings much less storage burden.
Source: doris.apache.org

Social recommendations and mentions

Based on our record, Amazon EMR should be more popular than Apache Doris. 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: over 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
View more

Apache Doris mentions (6)

  • Evolution of Data Sharding Towards Automation and Flexibility
    Like in many databases, Apache Doris shards data into partitions, and then a partition is further divided into buckets. Partitions are typically defined by time or other continuous values. This allows query engines to quickly locate the target data during queries by pruning irrelevant data ranges. - Source: dev.to / 10 months ago
  • Steps to industry-leading query speed: evolution of the Apache Doris execution engine
    What makes a modern database system? The three key modules are query optimizer, execution engine, and storage engine. Among them, the role of execution engine to the DBMS is like the chef to a restaurant. This article focuses on the execution engine of the Apache Doris data warehouse, explaining the secret to its high performance. - Source: dev.to / 10 months ago
  • Apache Doris for log and time series data analysis in NetEase, why not Elasticsearch and InfluxDB?
    For most people looking for a log management and analytics solution, Elasticsearch is the go-to choice. The same applies to InfluxDB for time series data analysis. These were exactly the choices of NetEase, one of the world's highest-yielding game companies but more than that. As NetEase expands its business horizons, the logs and time series data it receives explode, and problems like surging storage costs and... - Source: dev.to / 11 months ago
  • Multi-tenant workload isolation in Apache Doris: a better balance between isolation and utilization
    This is an in-depth introduction to the workload isolation capabilities of Apache Doris. But first of all, why and when do you need workload isolation? If you relate to any of the following situations, read on and you will end up with a solution:. - Source: dev.to / about 1 year ago
  • SQL Convertor for Easy Migration from Presto, Trino, ClickHouse, and Hive to Apache Doris
    Apache Doris is an all-in-one data platform that is capable of real-time reporting, ad-hoc queries, data lakehousing, log management and analysis, and batch data processing. As more and more companies have been replacing their component-heavy data architecture with Apache Doris, there is an increasing need for a more convenient data migration solution. That's why the Doris SQL Convertor is made. - Source: dev.to / about 1 year ago
View more

What are some alternatives?

When comparing Amazon EMR and Apache Doris, you can also consider the following products

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

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

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

StarRocks - StarRocks offers the next generation of real-time SQL engines for enterprise-scale analytics. Learn how we make it easy to deliver real-time analytics.

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

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

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