Amazon DynamoDB vs. Apache HBase

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon DynamoDB
Score 8.9 out of 10
N/A
Amazon DynamoDB is a cloud-native, NoSQL, serverless database service.
$0
capacity unit per hour
HBase
Score 7.3 out of 10
N/A
The Apache HBase project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable.N/A
Pricing
Amazon DynamoDBApache HBase
Editions & Modules
Provisioned - Read Operation
$0.00013
capacity unit per hour
Provisioned - Write Operation
$0.00065
capacity unit per hour
Provisioned - Global Tables
$0.000975
per Read Capacity
On-Demand Streams
$0.02
per 100,000 read operations
Provisioned - Streams
$0.02
per 100,000 read operations
On-Demand Data Requests Outside AWS Regions
$0.09
per GB
Provisioned - Data Requests Outside AWS Regions
$0.09
per GB
On-Demand Snapshot
$0.10
per GB per month
Provisioned - Snapshot
$0.10
per GB per month
On-Demand Restoring a Backup
$0.15
per GB
Provisioned - Restoring a Backup
$0.15
per GB
On-Demand Point-in-Time Recovery
$0.20
per GB per month
Provisioned - Point-in-Time Recovery
$0.20
per GB per month
On-Demand Read Operation
$0.25
per million requests
On-Demand Data Stored
$0.25
per GB per month
Provisioned - Data Stored
$0.25
per GB per month
On-Demand - Write Operation
$1.25
per million requests
On-Demand Global Tables
$1.875
per million write operations replicated
No answers on this topic
Offerings
Pricing Offerings
Amazon DynamoDBHBase
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon DynamoDBApache HBase
Considered Both Products
Amazon DynamoDB

No answer on this topic

HBase
Chose Apache HBase
Cassandra os great for writes. But with large datasets, depending, not as great as HBASE. Cassandra does support parquet now. HBase still performance issues. Cassandra has use cases of being used as time series. HBase, it fails miserably. GeoSpatial data, Hbase does work …
Top Pros
Top Cons
Features
Amazon DynamoDBApache HBase
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Amazon DynamoDB
9.2
70 Ratings
5% above category average
Apache HBase
7.7
5 Ratings
13% below category average
Performance9.269 Ratings7.15 Ratings
Availability9.470 Ratings7.85 Ratings
Concurrency8.868 Ratings7.05 Ratings
Security9.070 Ratings7.85 Ratings
Scalability9.469 Ratings8.65 Ratings
Data model flexibility8.667 Ratings7.15 Ratings
Deployment model flexibility10.023 Ratings8.25 Ratings
Best Alternatives
Amazon DynamoDBApache HBase
Small Businesses
IBM Cloudant
IBM Cloudant
Score 9.5 out of 10
IBM Cloudant
IBM Cloudant
Score 9.5 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 9.5 out of 10
IBM Cloudant
IBM Cloudant
Score 9.5 out of 10
Enterprises
IBM Cloudant
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Score 9.5 out of 10
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Score 9.5 out of 10
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User Ratings
Amazon DynamoDBApache HBase
Likelihood to Recommend
8.9
(80 ratings)
7.7
(10 ratings)
Likelihood to Renew
10.0
(34 ratings)
7.9
(10 ratings)
Usability
8.4
(4 ratings)
-
(0 ratings)
Performance
9.1
(44 ratings)
-
(0 ratings)
Support Rating
6.4
(4 ratings)
-
(0 ratings)
Product Scalability
9.1
(44 ratings)
-
(0 ratings)
User Testimonials
Amazon DynamoDBApache HBase
Likelihood to Recommend
Amazon AWS
It’s great for server less and real-time applications. It would be great for gaming and mobile apps. However, if you need relational database and have fixed budget, do not use it. While budget can be managed, you need to be careful. Also this is not a tool for storing big data, there are other wide-column database types you could use for it ins the ad
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Apache
Hbase is well suited for large organizations with millions of operations performing on tables, real-time lookup of records in a table, range queries, random reads and writes and online analytics operations. Hbase cannot be replaced for traditional databases as it cannot support all the features, CPU and memory intensive. Observed increased latency when using with MapReduce job joins.
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Pros
Amazon AWS
  • To manage varying workloads, it enables users to increase capacity as necessary and decrease it as needed.
  • Users can take advantage of its auto-scaling, in-memory caching, and backup without paying for the services of a database administrator.
  • We can use it for low scale operations.
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Apache
  • Scalability. HBase can scale to trillions of records.
  • Fast. HBase is extremely fast to scan values or retrieve individual records by key.
  • HBase can be accessed by standard SQL via Apache Phoenix.
  • Integrated. I can easily store and retrieve data from HBase using Apache Spark.
  • It is easy to set up DR and backups.
  • Ingest. It is easy to ingest data into HBase via shell, Java, Apache NiFi, Storm, Spark, Flink, Python and other means.
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Cons
Amazon AWS
  • Cost model may not be easy to control and may lead to higher costs if not carefully planned
  • Indexing may be a cost culprit when not planned, because it's not included on the data costs
  • The Query Language may not fulfill everybody's expectations, as it has less features than those of competitors.
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Apache
  • There are very few commands in HBase.
  • Stored procedures functionality is not available so it should be implemented.
  • HBase is CPU and Memory intensive with large sequential input or output access while as Map Reduce jobs are primarily input or output bound with fixed memory. HBase integrated with Map-reduce jobs will result in random latencies.
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Likelihood to Renew
Amazon AWS
It's core to our business, we couldn't survive without it. We use it to drive everything from FTP logins to processing stories and delivering them to clients. It's reliable and easy to query from all of our pipeline services. Integration with things like AWS Lambda makes it easy to trigger events and run code whenever something changes in the database.
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Apache
There's really not anything else out there that I've seen comparable for my use cases. HBase has never proven me wrong. Some companies align their whole business on HBase and are moving all of their infrastructure from other database engines to HBase. It's also open source and has a very collaborative community.
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Usability
Amazon AWS
Functionally, DynamoDB has the features needed to use it. The interface is not as easy to use, which impacts its usability. Being familiar with AWS in general is helpful in understanding the interface, however it would be better if the interface more closely aligned with traditional tools for managing datastores.
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Apache
No answers on this topic
Performance
Amazon AWS
It works very well across all the regions and response time is also very quick due to AWS's internal data transfer. Plus if your product requires HIPPA or some other regulations needs to be followed, you can easily replicate the DB into multiple regions and they manage all by it's own.
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Apache
No answers on this topic
Support Rating
Amazon AWS
I have not had to contact support for this service, however I have had to contact AWS for other services and their support has been good.
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Apache
No answers on this topic
Alternatives Considered
Amazon AWS
The only thing that can be compared to DynamoDB from the selected services can be Aurora. It is just that we use Aurora for High-Performance requirements as it can be 6 times faster than normal RDS DB. Both of them have served as well in the required scenario and we are very happy with most of the AWS services.
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Apache
Cassandra os great for writes. But with large datasets, depending, not as great as HBASE. Cassandra does support parquet now. HBase still performance issues. Cassandra has use cases of being used as time series. HBase, it fails miserably. GeoSpatial data, Hbase does work to an extent. HA between the two are almost the same.
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Scalability
Amazon AWS
I have taken one point away due to its size limits. In case the application requires queries, it becomes really complicated to read and write data. When it comes to extremely large data sets such as the case in my company, a third-party logistics company, where huge amount of data is generated on a daily basis, even though the scalability is good, it becomes difficult to manage all the data due to limits.
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Apache
No answers on this topic
Return on Investment
Amazon AWS
  • Some developers see DynamoDB and try to fit problems to it, instead of picking the best solution for a given problem. This is true of any newer tool that people are trying to adopt.
  • It has allowed us to add more scalability to some of our systems.
  • As with any new technology there was a ramp up/rework phase as we learned best practices.
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Apache
  • As Hbase is a noSql database, here we don't have transaction support and we cannot do many operations on the data.
  • Not having the feature of primary or a composite primary key is an issue as the architecture to be defined cannot be the same legacy type. Also the transaction concept is not applicable here.
  • The way data is printed on console is not so user-friendly. So we had to use some abstraction over HBase (eg apache phoenix) which means there is one new component to handle.
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ScreenShots

Amazon DynamoDB Screenshots

Screenshot of Amazon DynamoDB in the AWS Console