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Apache Spark VS AWS Database Migration Service

Compare Apache Spark VS AWS Database Migration Service and see what are their differences

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Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

AWS Database Migration Service logo AWS Database Migration Service

AWS Database Migration Service allows you to migrate to AWS quickly and securely. Learn more about the benefits and the key use cases.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • AWS Database Migration Service Landing page
    Landing page //
    2022-01-30

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

AWS Database Migration Service features and specs

  • Minimal Downtime
    AWS Database Migration Service ensures minimal downtime during the database migration process, making it ideal for applications that require continuous availability.
  • Supports Multiple Database Engines
    It supports migration of data between a wide variety of database engines including Oracle, Microsoft SQL Server, MySQL, MariaDB, PostgreSQL, and more.
  • Cost-Effective
    With a pay-as-you-go pricing model, users only pay for the compute resources used during the migration process, making it a cost-effective solution.
  • Managed Service
    As a fully managed service, it reduces the administrative overhead associated with database migrations, including hardware provisioning, software patching, and monitoring.
  • Continuous Data Replication
    It supports continuous data replication with high availability, allowing for nearly real-time data synchronization between the source and target databases.

Possible disadvantages of AWS Database Migration Service

  • Complex Initial Setup
    The initial setup and configuration can be complex, especially for users who are not familiar with AWS services and database migration processes.
  • Limited Customization
    Being a managed service, it offers limited customization options compared to self-managed solutions, which might be a drawback for users with specific requirements.
  • Latency Issues
    For large datasets, there might be latency issues during migration, depending on the network conditions and the geographical locations of the source and target databases.
  • Dependency on AWS Ecosystem
    The service is tightly integrated with AWS, which means it may not be as effective or easy to use with non-AWS environments, creating potential vendor lock-in.
  • Performance Overheads
    There may be performance overheads associated with running the migration tasks, which could impact the performance of the source or target databases during the migration process.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Analysis of AWS Database Migration Service

Overall verdict

  • Overall, AWS Database Migration Service is a reliable and efficient tool for migrating databases to the cloud, especially within the AWS ecosystem. Its flexibility, along with support for various database scenarios, makes it a worthwhile option for organizations looking to modernize their data infrastructure.

Why this product is good

  • AWS Database Migration Service (AWS DMS) is considered a good option for database migrations due to its ease of use, cost-effectiveness, and reliability. It supports a wide range of database engines and allows for seamless data migration with minimal downtime. The service enables continuous data replication, making it suitable for live migrations. Additionally, AWS DMS is fully managed, which means users don't need to worry about the underlying infrastructure, and it offers robust security features.

Recommended for

  • Organizations seeking to migrate their on-premises databases to AWS with minimal downtime.
  • Businesses looking to replicate their data across different regions or availability zones.
  • Users who require a scalable and managed database migration solution.
  • Enterprises wanting to transform their database infrastructure into a cloud-native architecture.
  • Teams that need to conduct continuous data replication from source to target databases.

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

AWS Database Migration Service videos

+ Add

AWS Database Migration Service (DMS)

Category Popularity

0-100% (relative to Apache Spark and AWS Database Migration Service)

Apache Spark

AWS Database Migration Service

Databases
100 100%
Databases
0% 0
Data Integration
0 0%
Data Integration
100% 100
Big Data
100 100%
Big Data
0% 0
ETL
0 0%
ETL
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 Apache Spark and AWS Database Migration Service

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

AWS Database Migration Service Reviews

Best ETL Tools: A Curated List
Mostly Batch: Matillion ETL had some real-time CDC based on Amazon DMS that has been deprecated. The Data Loader does have some CDC, but overall, the Data Loader is limited in functionality, and if it’s based on DMS, it will have the limitations of DMS as well.
Source: estuary.dev

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than AWS Database Migration Service. It has been mentiond 72 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.

Apache Spark mentions (72)

  • Gravitino - the unified metadata lake
    In the meantime, other query engine support is on the roadmap, including Apache Spark, Apache Flink, and others. - Source: dev.to / about 2 months ago
  • Introducing RisingWave's Hosted Iceberg Catalog-No External Setup Needed
    Because the hosted catalog is a standard JDBC catalog, tools like Spark, Trino, and Flink can still access your tables. For example:. - Source: dev.to / 3 months ago
  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 5 months ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 6 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 7 months ago
View more

AWS Database Migration Service mentions (31)

  • Choosing the right, real-time, Postgres CDC platform
    The major infrastructure providers offer CDC products that work within their ecosystem. Tools like AWS DMS, GCP Datastream, and Azure Data Factory can be configured to stream changes from Postgres to other infrastructure. - Source: dev.to / 10 months ago
  • 3 Proven Patterns for Reporting with Serverless
    The second big drawback is speed. There will be more latency in this scenario. How much latency depends upon the environment. If there is RDBMS in the source, AWS Data Migration Service will at worst take around 60 seconds to replicate. That cost needs to be accounted for. Secondarily, many triggering events are leveraged which happen fairly quickly but they do add up. - Source: dev.to / over 1 year ago
  • RDS Database Migration Series - A horror story of using AWS DMS with a happy ending
    Amazon Database Migration Service might initially seem like a perfect tool for a smooth and straightforward migration to RDS. However, our overall experience using it turned out to be closer to an open beta product rather than a production-ready tool for dealing with a critical asset of any company, which is its data. Nevertheless, with the extra adjustments, we made it work for almost all our needs. - Source: dev.to / over 1 year ago
  • Aurora serverless v1 to v2 upgrade pointers?
    Does AWS DMS make sense here? Doesn't the aforementioned "snapshot+restore to provisioned and upgrade" method suffice? I wanted to get some opinions before deep diving into the docs for yet another AWS service. Source: about 2 years ago
  • Using Amazon RDS Postgres as a read replica from an external Database
    One easy solution is AWS DMS. I use it for on-going CDC replication with custom transforms, but you can use it for simple replication too. Source: over 2 years ago
View more

What are some alternatives?

When comparing Apache Spark and AWS Database Migration Service, you can also consider the following products

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

AWS Glue - Fully managed extract, transform, and load (ETL) service

Hadoop - Open-source software for reliable, scalable, distributed computing

Xplenty - Xplenty is the #1 SecurETL - allowing you to build low-code data pipelines on the most secure and flexible data transformation platform. No longer worry about manual data transformations. Start your free 14-day trial now.

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

Skyvia - Free cloud data platform for data integration, backup & management

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