Amazon DynamoDBย vsย Google BigQuery

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Amazon DynamoDB
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Amazon DynamoDB vs Google BigQuery: What are the differences?

What is Amazon DynamoDB? Fully managed NoSQL database service. All data items are stored on Solid State Drives (SSDs), and are replicated across 3 Availability Zones for high availability and durability. With DynamoDB, 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.

What is Google BigQuery? Analyze terabytes of data in seconds. 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..

Amazon DynamoDB can be classified as a tool in the "NoSQL Database as a Service" category, while Google BigQuery is grouped under "Big Data as a Service".

Some of the features offered by Amazon DynamoDB are:

  • Automated Storage Scaling โ€“ 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 โ€“ 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. If your throughput requirements change, simply update your table's request capacity using the AWS Management Console or the Amazon DynamoDB APIs. You are still able to achieve your prior throughput levels while scaling is underway.
  • Fully Distributed, Shared Nothing Architecture โ€“ Amazon DynamoDB scales horizontally and can seamlessly scale a single table over hundreds of servers.

On the other hand, Google BigQuery provides the following key features:

  • 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.

"Predictable performance and cost" is the primary reason why developers consider Amazon DynamoDB over the competitors, whereas "High Performance" was stated as the key factor in picking Google BigQuery.

Netflix, Medium, and Lyft are some of the popular companies that use Amazon DynamoDB, whereas Google BigQuery is used by Spotify, Sentry, and Vine Labs. Amazon DynamoDB has a broader approval, being mentioned in 444 company stacks & 187 developers stacks; compared to Google BigQuery, which is listed in 160 company stacks and 41 developer stacks.

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What is Amazon DynamoDB?

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.

What is Google BigQuery?

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.
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What are some alternatives to Amazon DynamoDB and Google BigQuery?
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.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Amazon SimpleDB
Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.
Amazon S3
Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
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Decisions about Amazon DynamoDB and Google BigQuery
Nick Rockwell
Nick Rockwell
CTO at NY Times ยท | 5 upvotes ยท 11.7K views
atThe New York TimesThe New York Times
Amazon DynamoDB
Amazon DynamoDB
Google Cloud Dataflow
Google Cloud Dataflow
Google Cloud Pub/Sub
Google Cloud Pub/Sub
Google BigQuery
Google BigQuery
#AnalyticsPipeline
#Analytics

We really drank the Google Kool-Aid on analytics. So, everything's going into Google BigQuery and almost everything is going straight into Google Cloud Pub/Sub and then doing some processing in Google Cloud Dataflow before ending up in BigQuery. We still do too much processing and augmentation on the front end before it goes into Pub/Sub. And that's using some kind of stuff we pulled together using Amazon DynamoDB and so on. And it's very brittle, actually. Actually, Dynamo throttling is one of our biggest headaches. So, I want all of that to go away and do all our augmentation in BigQuery after the data's been collected. And having it just go straight into Pub/Sub. So, we're working on that. And it'll happen, some time. #Analytics #AnalyticsPipeline

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Snowflake
Snowflake
Google BigQuery
Google BigQuery

I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

What's nice too is that it has SQL-based ML tools, and it has great GIS support!

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Doru Mihai
Doru Mihai
Solution Architect ยท | 4 upvotes ยท 454 views
Amazon DynamoDB
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.

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How developers use Amazon DynamoDB and Google BigQuery
Avatar of Karma
Karma uses Amazon DynamoDBAmazon DynamoDB

For most of the stuff we use MySQL. We just use Amazon RDS. But for some stuff we use Amazon DynamoDB. We love DynamoDB. It's amazing. We store usage data in there, for example. I think we have close to seven or eight hundred million records in there and it's scaled like you don't even notice it. You never notice any performance degradation whatsoever. It's insane, and the last time I checked we were paying $150 bucks for that.

Avatar of Volkan ร–zรงelik
Volkan ร–zรงelik uses Amazon DynamoDBAmazon DynamoDB

zerotoherojs.com โ€™s userbase, and course details are stored in DynamoDB tables.

The good thing about AWS DynamoDB is: For the amount of traffic that I have, it is free. It is highly-scalable, it is managed by Amazon, and it is pretty fast.

It is, again, one less thing to worry about (when compared to managing your own MongoDB elsewhere).

Avatar of ShareThis
ShareThis uses Google BigQueryGoogle BigQuery

BigQuery allows our team to pull reports quickly using a SQL-like queries against our large store of data about social sharing. We use the information throughout the company, to do everything from making internal product decisions based on usage patterns to sharing certain kinds of custom reports with our publishers.

Avatar of CloudRepo
CloudRepo uses Amazon DynamoDBAmazon DynamoDB

We store customer metadata in DynamoDB. We decided to use Amazon DynamoDB because it was a fully managed, highly available solution. We didn't want to operate our own SQL server and we wanted to ensure that we built CloudRepo on high availability components so that we could pass that benefit back to our customers.

Avatar of Lyndon Wong
Lyndon Wong uses Google BigQueryGoogle BigQuery

Aggregation of user events and traits across a marketing website, SaaS web application, user account provisioning backend and Salesforce CRM. Enables full-funnel analysis of campaign ROI, customer acquisition, engagement and retention at both the user and target account level.

Avatar of nrise
nrise uses Amazon DynamoDBAmazon DynamoDB

๋ช‡๋ช‡ ๋กœ๊ทธ๋Š” ํ˜„์žฌ AWS DynamoDB ์— ๊ธฐ๋ก๋˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๊ฐœ์„ ์„ ํ†ตํ•ด mongodb ๋กœ ์˜ฎ๊ธธ ๊ณ„ํš์„ ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ์•„์ฃผ ๊ฐ„๋‹จํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์Œ“๋Š” ์šฉ๋„๋กœ๋Š” ๋‚˜์˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. ๋‹ค๋งŒ, ์ฟผ๋ฆฌ๊ฐ€ ์•„์ฃผ ์ œํ•œ์ ์ž…๋‹ˆ๋‹ค. ์‚ฌ์šฉํ•˜๊ธฐ ์ „์— ๋ฐ˜๋“œ์‹œ DynamoDB ์˜ ์ŠคํŽ™์„ ํ™•์ธํ•  ํ•„์š”๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค.

Avatar of Blue Shell Games
Blue Shell Games uses Google BigQueryGoogle BigQuery

Google's insanely fast, feature-rich, zero-maintenance column store. Used for real-time customer data queries.

Avatar of HyperTrack
HyperTrack uses Amazon DynamoDBAmazon DynamoDB

To store device health records as it allows super fast writes and range queries.

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