Amazon DynamoDB vs Google BigQuery

Overview

Amazon DynamoDB

Stacks4.0K

Followers3.2K

Votes195

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Amazon DynamoDB vs Google BigQuery: What are the differences?

Introduction

In this article, we will compare Amazon DynamoDB and Google BigQuery, two popular data storage and analysis solutions, to understand their key differences and use cases.

Data Structure and Querying Language: Amazon DynamoDB is a NoSQL database that supports key-value and document data models, making it suitable for unstructured and semi-structured data. On the other hand, Google BigQuery is a fully-managed, serverless data warehouse that excels in processing structured data using SQL-like queries.
Scalability and Performance: DynamoDB offers automatic scaling with seamless data distribution, allowing it to handle massive workloads and provide high throughput with low latency. BigQuery also scales automatically and can enable petabyte-scale querying, but its performance may vary and require careful partitioning and optimization for optimal results.
Storage and Cost Model: DynamoDB charges for provisioned capacity and additional features like backups and Global Tables. BigQuery follows a pay-per-query pricing model based on the amount of data processed, with separate charges for storage and queries. DynamoDB's pricing can be advantageous for steady or predictable workloads, while BigQuery's model is more suitable for ad-hoc and analytical workloads.
Data Integration and Ecosystem: DynamoDB integrates tightly with other Amazon Web Services (AWS) services, allowing seamless integration and data ETL workflows. BigQuery has connectors with various Google Cloud Platform tools and external data sources, simplifying data ingestion and analysis from different systems.
Security and Access Control: DynamoDB provides fine-grained access control with AWS Identity and Access Management (IAM) policies, allowing granular control over table and item-level permissions. BigQuery offers similar access control mechanisms but with Google Cloud IAM, enabling secure data sharing and collaboration within organizations.
Data Analytics Capabilities: While DynamoDB provides basic querying capabilities, BigQuery offers more advanced analytics features like window functions, nested and repeated data handling, and machine learning integrations. BigQuery is optimized for complex analytical queries, making it suitable for data exploration, ad-hoc analysis, and business intelligence.

In Summary, Amazon DynamoDB is a NoSQL database with flexible data models, excellent scalability, and deep integration with AWS services. On the other hand, Google BigQuery is a versatile data warehouse that excels in structured data processing, ad-hoc querying, and advanced analytics capabilities.

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Advice on Amazon DynamoDB, Google BigQuery

Doru

Solution Architect

Jun 9, 2019

Reviewon

Amazon DynamoDB

I use Amazon DynamoDB because it integrates seamlessly with other AWS SaaS solutions and if cost is the primary concern early on, then this will be a better choice when compared to AWS RDS or any other solution that requires the creation of a HA cluster of IaaS components that will cost money just for being there, the costs not being influenced primarily by usage.

1.37k views1.37k

Comments

akash

Aug 27, 2020

Needs adviceon

Cloud Firestore

Firebase Realtime Database

Amazon DynamoDB

We are building a social media app, where users will post images, like their post, and make friends based on their interest. We are currently using Cloud Firestore and Firebase Realtime Database. We are looking for another database like Amazon DynamoDB; how much this decision can be efficient in terms of pricing and overhead?

199k views199k

Comments

Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

193k views193k

Comments

Detailed Comparison

Amazon DynamoDB	Google BigQuery
With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.	Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs;Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity;Fully Distributed, Shared Nothing Architecture	All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.
Statistics
Stacks 4.0K	Stacks 1.8K
Followers 3.2K	Followers 1.5K
Votes 195	Votes 152
Pros & Cons
Pros 62 Predictable performance and cost 56 Scalable 35 Native JSON Support 21 AWS Free Tier 7 Fast Cons 4 Only sequential access for paginate data 1 Scaling 1 Document Limit Size	Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas
Integrations
Amazon RDS for PostgreSQL PostgreSQL MySQL SQLite Azure Database for MySQL	Xplenty Fluentd Looker Chartio Treasure Data

What are some alternatives to Amazon DynamoDB, Google BigQuery?

Azure Cosmos DB

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Cloudant

Cloudant’s distributed database as a service (DBaaS) allows developers of fast-growing web and mobile apps to focus on building and improving their products, instead of worrying about scaling and managing databases on their own.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Google Cloud Bigtable

Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.

Google Cloud Datastore

Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.

Related Comparisons

Amazon DynamoDB vs Google BigQuery: What are the differences?

Introduction

In this article, we will compare Amazon DynamoDB and Google BigQuery, two popular data storage and analysis solutions, to understand their key differences and use cases.

Data Structure and Querying Language: Amazon DynamoDB is a NoSQL database that supports key-value and document data models, making it suitable for unstructured and semi-structured data. On the other hand, Google BigQuery is a fully-managed, serverless data warehouse that excels in processing structured data using SQL-like queries.
Scalability and Performance: DynamoDB offers automatic scaling with seamless data distribution, allowing it to handle massive workloads and provide high throughput with low latency. BigQuery also scales automatically and can enable petabyte-scale querying, but its performance may vary and require careful partitioning and optimization for optimal results.
Storage and Cost Model: DynamoDB charges for provisioned capacity and additional features like backups and Global Tables. BigQuery follows a pay-per-query pricing model based on the amount of data processed, with separate charges for storage and queries. DynamoDB's pricing can be advantageous for steady or predictable workloads, while BigQuery's model is more suitable for ad-hoc and analytical workloads.
Data Integration and Ecosystem: DynamoDB integrates tightly with other Amazon Web Services (AWS) services, allowing seamless integration and data ETL workflows. BigQuery has connectors with various Google Cloud Platform tools and external data sources, simplifying data ingestion and analysis from different systems.
Security and Access Control: DynamoDB provides fine-grained access control with AWS Identity and Access Management (IAM) policies, allowing granular control over table and item-level permissions. BigQuery offers similar access control mechanisms but with Google Cloud IAM, enabling secure data sharing and collaboration within organizations.
Data Analytics Capabilities: While DynamoDB provides basic querying capabilities, BigQuery offers more advanced analytics features like window functions, nested and repeated data handling, and machine learning integrations. BigQuery is optimized for complex analytical queries, making it suitable for data exploration, ad-hoc analysis, and business intelligence.

Amazon DynamoDB vs Google BigQuery

Overview

Amazon DynamoDB vs Google BigQuery: What are the differences?