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Google Cloud Dataflow VS Amazon Elasticsearch Service

Compare Google Cloud Dataflow VS Amazon Elasticsearch Service and see what are their differences

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Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Google Cloud Dataflow logo Google Cloud Dataflow

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

Amazon Elasticsearch Service logo Amazon Elasticsearch Service

Amazon Elasticsearch Service is a managed service that makes it easy to deploy, operate, and scale Elasticsearch in the AWS Cloud.
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03
  • Amazon Elasticsearch Service Landing page
    Landing page //
    2023-03-13

Google Cloud Dataflow features and specs

  • Scalability
    Google Cloud Dataflow can automatically scale up or down depending on your data processing needs, handling massive datasets with ease.
  • Fully Managed
    Dataflow is a fully managed service, which means you don't have to worry about managing the underlying infrastructure.
  • Unified Programming Model
    It provides a single programming model for both batch and streaming data processing using Apache Beam, simplifying the development process.
  • Integration
    Seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Bigtable.
  • Real-time Analytics
    Supports real-time data processing, enabling quicker insights and facilitating faster decision-making.
  • Cost Efficiency
    Pay-as-you-go pricing model ensures you only pay for resources you actually use, which can be cost-effective.
  • Global Availability
    Cloud Dataflow is available globally, which allows for regionalized data processing.
  • Fault Tolerance
    Built-in fault tolerance mechanisms help ensure uninterrupted data processing.

Possible disadvantages of Google Cloud Dataflow

  • Steep Learning Curve
    The complexity of using Apache Beam and understanding its model can be challenging for beginners.
  • Debugging Difficulties
    Debugging data processing pipelines can be complex and time-consuming, especially for large-scale data flows.
  • Cost Management
    While it can be cost-efficient, the costs can rise quickly if not monitored properly, particularly with real-time data processing.
  • Vendor Lock-in
    Using Google Cloud Dataflow can lead to vendor lock-in, making it challenging to migrate to another cloud provider.
  • Limited Support for Non-Google Services
    While it integrates well within Google Cloud, support for non-Google services may not be as robust.
  • Latency
    There can be some latency in data processing, especially when dealing with high volumes of data.
  • Complexity in Pipeline Design
    Designing pipelines to be efficient and cost-effective can be complex, requiring significant expertise.

Amazon Elasticsearch Service features and specs

  • Scalability
    Amazon OpenSearch Service allows for easy scalability of clusters based on demand, without the need to manually manage infrastructure.
  • Managed Service
    The service is fully managed by AWS, including automatic backups, monitoring, and updates, reducing operational overhead for users.
  • Integration
    Seamless integration with other AWS services such as Amazon VPC, AWS Lambda, and Amazon S3 for streamlined workflows and enhanced data analysis capabilities.
  • Security
    Built-in security features such as VPC support, IAM policies, and data encryption at rest and in transit ensure data is well-protected.
  • High Availability
    The service offers multiple availability zones for high availability and durability of data.
  • Cost Efficiency
    Comes with a pay-as-you-go pricing model which allows users to efficiently manage costs and scale resources according to budget and usage.

Possible disadvantages of Amazon Elasticsearch Service

  • Vendor Lock-in
    Relying on Amazon for Elasticsearch service can lead to vendor lock-in, making it hard to transition to other services or platforms without significant effort.
  • Cost
    While the pricing model is flexible, the costs can accumulate quickly with large-scale deployments, potentially leading to high expenses.
  • Limited Customization
    Being a managed service, it offers less flexibility and customization options compared to self-managed solutions.
  • Version Lag
    The service may not always be in sync with the latest releases and may lag behind the open-source Elasticsearch in terms of new features.
  • Complexity
    Setting up and optimizing Amazon OpenSearch Service can be complex, requiring a good understanding of both the service and underlying Elasticsearch technology.

Google Cloud Dataflow videos

+ Add

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • Review - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • Review - Apache Beam and Google Cloud Dataflow

Amazon Elasticsearch Service videos

+ Add

Amazon Elasticsearch Service Deep Dive - AWS Online Tech Talks

More videos:

  • Review - Moving From Self-Managed Elasticsearch to Amazon Elasticsearch Service - AWS Online Tech Talks

Category Popularity

0-100% (relative to Google Cloud Dataflow and Amazon Elasticsearch Service)

Google Cloud Dataflow

Amazon Elasticsearch Service

Big Data
100 100%
Big Data
0% 0
Custom Search Engine
0 0%
Custom Search Engine
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0
Custom Search
0 0%
Custom Search
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 Google Cloud Dataflow and Amazon Elasticsearch Service

Google Cloud Dataflow Reviews

Top 8 Apache Airflow Alternatives in 2024
Google Cloud Dataflow is highly focused on real-time streaming data and batch data processing from web resources, IoT devices, etc. Data gets cleansed and filtered as Dataflow implements Apache Beam to simplify large-scale data processing. Such prepared data is ready for analysis for Google BigQuery or other analytics tools for prediction, personalization, and other purposes.
Source: blog.skyvia.com

Amazon Elasticsearch Service Reviews

We have no reviews of Amazon Elasticsearch Service yet.
Be the first one to post

Social recommendations and mentions

Google Cloud Dataflow might be a bit more popular than Amazon Elasticsearch Service. We know about 14 links to it since March 2021 and only 11 links to Amazon Elasticsearch Service. 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.

Google Cloud Dataflow mentions (14)

  • How do you implement CDC in your organization
    Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 2 years ago
  • Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!
    This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 2 years ago
  • How are view/listen counts rolled up on something like Spotify/YouTube?
    I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 2 years ago
  • Best way to export several GCP datasets to AWS?
    You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 2 years ago
  • Why we don’t use Spark
    It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 3 years ago
View more

Amazon Elasticsearch Service mentions (11)

  • OpenSearch for humans
    This change triggered a response from Amazon Web Services, which offered OpenSearch (data store and search engine) and OpenSearch Dashboards (visualization and user interface) as Apache2.0 licensed open-source projects. - Source: dev.to / about 1 year ago
  • OpenSearch as Vector DB: Supercharge Your LLM
    Amazon OpenSearch Service allows you to deploy a secured OpenSearch cluster in minutes. - Source: dev.to / almost 2 years ago
  • Building Serverless Applications with AWS - Data
    If yes to these, then OpenSearch is where you are looking. I rarely ever use OpenSearch on its own but usually pair it with DynamoDB. The performance of DDB and the power of searching with OpenSearch make a nice combination. And as with most things with Serverless, pick the right tool for the job. And when it comes to Data, there are so many choices because each one of these is specific to the problem it solves. - Source: dev.to / almost 2 years ago
  • Advice on a simple database architecture
    Have you looked into Amazon OpenSearch Service (https://aws.amazon.com/opensearch-service/)? You should be able to load the log files into that service and then query it there. Should simplify things a lot. Source: about 2 years ago
  • AWS Beginner's Key Terminologies
    Elasticsearch (analytics) An open-source, real-time distributed search and analytics engine used for full-text search, structured search, and analytics. OpenSearch was developed by the Elastic company. Amazon OpenSearch Service (OpenSearch Service) is an AWS-managed service for deploying, operating and scaling OpenSearch in the AWS Cloud. Https://aws.amazon.com/opensearch-service/. - Source: dev.to / over 2 years ago
View more

What are some alternatives?

When comparing Google Cloud Dataflow and Amazon Elasticsearch Service, you can also consider the following products

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

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

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

PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time

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

ElasticSearch - Elasticsearch is an open source, distributed, RESTful search engine.

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