Elasticsearch vs Google Cloud Dataflow

Overview

Elasticsearch

Stacks35.5K

Followers27.1K

Votes1.6K

Google Cloud Dataflow

Stacks219

Followers497

Votes19

Elasticsearch vs Google Cloud Dataflow: What are the differences?

Key Differences Between Elasticsearch and Google Cloud Dataflow

Elasticsearch and Google Cloud Dataflow are two different technologies used in data processing and analysis. Here are the key differences between them:

Data Processing Paradigm: Elasticsearch is a distributed search and analytics engine that is designed for real-time data exploration and analysis. It is specifically optimized for searching and aggregating large volumes of data. On the other hand, Google Cloud Dataflow is a fully managed service for building and executing batch and stream data processing pipelines. It provides a unified programming model for both batch and stream processing, making it flexible for different use cases.
Data Storage: Elasticsearch stores and indexes data in a distributed manner, making it highly scalable and efficient for search and analytics. It uses Apache Lucene under the hood for full-text indexing and searching. In contrast, Google Cloud Dataflow does not provide its own data storage. Instead, it integrates with various data storage solutions like Google Cloud Storage, BigQuery, and Pub/Sub. This allows users to process data from different sources and store the results in different formats.
Data Flow and Transformation: Elasticsearch provides powerful search capabilities and supports various query types like full-text search, filtering, and aggregations. It enables real-time analytics by indexing and analyzing data as it flows into the system. On the other hand, Google Cloud Dataflow focuses on the transformation of data using a dataflow model based on parallel processing. It provides a rich set of transformations and windowing options for data manipulation.
Ecosystem and Integration: Elasticsearch has a vibrant ecosystem with a wide range of plugins and extensions available. It integrates well with other tools and frameworks like Kibana (for visualization), Logstash (for data ingestion), and Beats (for lightweight data shippers). Google Cloud Dataflow is part of the larger Google Cloud ecosystem and integrates seamlessly with other services like BigQuery, Cloud Pub/Sub, and Datastore. It also supports integration with external libraries and frameworks through its SDKs.
Managed Service vs Self-hosted: Elasticsearch can be self-hosted or run on cloud platforms like AWS, Azure, or Google Cloud. Users are responsible for managing and scaling their Elasticsearch clusters. In contrast, Google Cloud Dataflow is a fully managed service provided by Google Cloud. It abstracts the infrastructure management, allowing users to focus on writing data processing logic without worrying about cluster setup and maintenance.
Pricing Model: Elasticsearch is open-source and comes with a basic free version. However, additional features and support may require a subscription or license. The pricing is based on factors like the number of nodes and cores in the cluster. Google Cloud Dataflow, being a managed service, follows a pay-as-you-go model. Users are billed based on the resources consumed by their pipelines, including the amount of data processed and the processing time.

In summary, Elasticsearch is a distributed search and analytics engine optimized for real-time data exploration, while Google Cloud Dataflow is a fully managed service for building and executing data processing pipelines with a unified programming model for both batch and stream processing.

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Advice on Elasticsearch, Google Cloud Dataflow

Rana Usman

Chief Technology Officer at TechAvanza

Jun 4, 2020

Needs adviceon

Firebase

Elasticsearch

Algolia

Hey everybody! (1) I am developing an android application. I have data of around 3 million record (less than a TB). I want to save that data in the cloud. Which company provides the best cloud database services that would suit my scenario? It should be secured, long term useable, and provide better services. I decided to use Firebase Realtime database. Should I stick with Firebase or are there any other companies that provide a better service?

(2) I have the functionality of searching data in my app. Same data (less than a TB). Which search solution should I use in this case? I found Elasticsearch and Algolia search. It should be secure and fast. If any other company provides better services than these, please feel free to suggest them.

Thank you!

408k views408k

Comments

André

Nov 20, 2020

Needs adviceon

Elasticsearch

Amazon DynamoDB

Hi, community, I'm planning to build a web service that will perform a text search in a data set off less than 3k well-structured JSON objects containing config data. I'm expecting no more than 20 MB of data. The general traits I need for this search are:

Typo tolerant (fuzzy query), so it has to match the entries even though the query does not match 100% with a word on that JSON
Allow a strict match mode
Perform the search through all the JSON values (it can reach 6 nesting levels)
Ignore all Keys of the JSON; I'm interested only in the values.

The only thing I'm researching at the moment is Elasticsearch, and since the rest of the stack is on AWS the Amazon ElasticSearch is my favorite candidate so far. Although, the only knowledge I have on it was fetched from some articles and Q&A that I read here and there. Is ElasticSearch a good path for this project? I'm also considering Amazon DynamoDB (which I also don't know of), but it does not look to cover the requirements of fuzzy-search and ignore the JSON properties. Thank you in advance for your precious advice!

60.3k views60.3k

Comments

Ted

Computer Science

Dec 19, 2020

Review

I think elasticsearch should be a great fit for that use case. Using the AWS version will make your life easier. With such a small dataset you may also be able to use an in process library for searching and possibly remove the overhead of using a database. I don’t if it fits the bill, but you may also want to look into lucene.

I can tell you that Dynamo DB is definitely not a good fit for your use case. There is no fuzzy matching feature and you would need to have an index for each field you want to search or convert your data into a more searchable format for storing in Dynamo, which is something a full text search tool like elasticsearch is going to do for you.

42.9k views42.9k

Comments

Detailed Comparison

Elasticsearch	Google Cloud Dataflow
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).	Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
Distributed and Highly Available Search Engine;Multi Tenant with Multi Types;Various set of APIs including RESTful;Clients available in many languages including Java, Python, .NET, C#, Groovy, and more;Document oriented;Reliable, Asynchronous Write Behind for long term persistency;(Near) Real Time Search;Built on top of Apache Lucene;Per operation consistency;Inverted indices with finite state transducers for full-text querying;BKD trees for storing numeric and geo data;Column store for analytics;Compatible with Hadoop using the ES-Hadoop connector;Open Source under Apache 2 and Elastic License	Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;
Statistics
Stacks 35.5K	Stacks 219
Followers 27.1K	Followers 497
Votes 1.6K	Votes 19
Pros & Cons
Pros 329 Powerful api 315 Great search engine 231 Open source 214 Restful 200 Near real-time search Cons 7 Resource hungry 6 Diffecult to get started 5 Expensive 4 Hard to keep stable at large scale	Pros 7 Unified batch and stream processing 5 Autoscaling 4 Fully managed 3 Throughput Transparency
Integrations
Kibana Beats Logstash	No integrations available

What are some alternatives to Elasticsearch, Google Cloud Dataflow?

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

Azure Search

Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.

Swiftype

Swiftype is the easiest way to add great search to your website or mobile application.

MeiliSearch

It is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.

Quickwit

It is the next-gen search & analytics engine built for logs. It is designed from the ground up to offer cost-efficiency and high reliability on large data sets. Its benefits are most apparent in multi-tenancy or multi-index settings.

Amazon Kinesis

Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

Related Comparisons

Elasticsearch vs Google Cloud Dataflow: What are the differences?

Key Differences Between Elasticsearch and Google Cloud Dataflow

Elasticsearch and Google Cloud Dataflow are two different technologies used in data processing and analysis. Here are the key differences between them:

Data Processing Paradigm: Elasticsearch is a distributed search and analytics engine that is designed for real-time data exploration and analysis. It is specifically optimized for searching and aggregating large volumes of data. On the other hand, Google Cloud Dataflow is a fully managed service for building and executing batch and stream data processing pipelines. It provides a unified programming model for both batch and stream processing, making it flexible for different use cases.
Data Storage: Elasticsearch stores and indexes data in a distributed manner, making it highly scalable and efficient for search and analytics. It uses Apache Lucene under the hood for full-text indexing and searching. In contrast, Google Cloud Dataflow does not provide its own data storage. Instead, it integrates with various data storage solutions like Google Cloud Storage, BigQuery, and Pub/Sub. This allows users to process data from different sources and store the results in different formats.
Data Flow and Transformation: Elasticsearch provides powerful search capabilities and supports various query types like full-text search, filtering, and aggregations. It enables real-time analytics by indexing and analyzing data as it flows into the system. On the other hand, Google Cloud Dataflow focuses on the transformation of data using a dataflow model based on parallel processing. It provides a rich set of transformations and windowing options for data manipulation.
Ecosystem and Integration: Elasticsearch has a vibrant ecosystem with a wide range of plugins and extensions available. It integrates well with other tools and frameworks like Kibana (for visualization), Logstash (for data ingestion), and Beats (for lightweight data shippers). Google Cloud Dataflow is part of the larger Google Cloud ecosystem and integrates seamlessly with other services like BigQuery, Cloud Pub/Sub, and Datastore. It also supports integration with external libraries and frameworks through its SDKs.
Managed Service vs Self-hosted: Elasticsearch can be self-hosted or run on cloud platforms like AWS, Azure, or Google Cloud. Users are responsible for managing and scaling their Elasticsearch clusters. In contrast, Google Cloud Dataflow is a fully managed service provided by Google Cloud. It abstracts the infrastructure management, allowing users to focus on writing data processing logic without worrying about cluster setup and maintenance.
Pricing Model: Elasticsearch is open-source and comes with a basic free version. However, additional features and support may require a subscription or license. The pricing is based on factors like the number of nodes and cores in the cluster. Google Cloud Dataflow, being a managed service, follows a pay-as-you-go model. Users are billed based on the resources consumed by their pipelines, including the amount of data processed and the processing time.

Elasticsearch vs Google Cloud Dataflow

Overview

Elasticsearch vs Google Cloud Dataflow: What are the differences?