DynamoDb and BigTable are suggested for low-latency access use cases. Both are from query-driven database families requiring you to define a schema and primary keys based on query patterns. Google Bigtable and Amazon DynamoDB belong to NoSQL Database as a Service category of the tech stack. Not sure which option is better for your needs: Google Cloud Bigtable vs Amazon DynamoDB? No issue. Here, we’ll contrast the two services based on many criteria.
What is Google BigTable?
Google Bigtable was created to enable applications needing tremendous scalability. The database can handle petabytes of data and utilizes a simple data format – a sparse, distributed, persistent multidimensional sorted map. Data is put together in chronological sequence using the row key, and the indexing of the map is set up using the row, column, and timestamp keys.
High capacity is made possible through compression methods. BigTable is used by different Google applications like Google Analytics, Google Earth, Google search, and App Engine.
Benefits and key features of Google BigTable
Bigtable enables low latency and high write and reads throughput for quick access to massive volumes of data and is perfect for storing petabytes of data in a key-value store. Throughput increases linearly; adding more Bigtable nodes may improve QPS (queries per second).
You may quickly expand up to hundreds of nodes serving peak demand from only one node per cluster at first. For live apps, replication offers workload segregation and high availability.
Cluster Resize Without Downtime
You can change Bigtable throughput dynamically without downtime by adding or deleting cluster nodes. As a result, you can expand the Bigtable cluster to handle a heavy demand, then decrease the size again to save cost. And there will be no downtime.
Data written in BigTable will automatically duplicate where necessary with consistency. There is no need for manual processes to guarantee consistency, fix data, or synchronize writes and deletes.
What is DynamoDB?
A NoSQL database service, Amazon DynamoDB offers quick and reliable performance and seamless scaling. As a result, you may free yourself from worrying about infrastructure, database setup, backup, replication, scalability, and management. Everything is taken care of by DynamoDB.
Additionally, DynamoDB provides encryption at rest, which removes the operational complexity and effort of securing sensitive data.
DynamoDB tables can handle any traffic volume and store and retrieve any quantity of data. The throughput capacity of a table can be increased or decreased without causing downtime or performance deterioration. Furthermore, you can track the performance and resource usage from the AWS console.
DynamoDB offers on-demand backup functionality. You may use it to complete backups of your tables for long-term storage and archiving to meet regulatory compliance requirements.
Benefits and key features of Amazon DynamoDB
Scalability and performance
Scaling databases can be risky and challenging, as anybody who has worked in the IT business knows. By monitoring how close your consumption is to the top constraints, DynamoDB allows you to auto-scale. This can help you prevent performance problems and cut expenses by enabling your system to react to the volume of data flow.
Effective access control becomes crucial as data grows more personal and particular. Without impeding other people’s productivity, you want to be able to grant access control to the appropriate individuals quickly. The owner may exert more control over the data in the table.
Using the TTL, you may establish timestamps for eliminating outdated data from your tables. The data that has been designated to expire is removed from the database once the timestamp is expired. Developers can keep track of expired data and erase it automatically. This procedure aids in decreasing storage requirements and lowering the price of manual data deletion labor.
Google Bigtable vs Amazon DynamoDB: Difference between Google BigTable and DynamoDB
Now, you have a better understanding of both services. So let’s look at some of the primary differences between them.
All you need to do with DynamoDB’s serverless design is specify a table’s write and read capacity. However, there is a bit of additional setup required for BigTable. You must define an instance in addition to the cluster in charge of storing the data.
You can determine how the user will interact with an instance by configuring different components, such as storage type. DynamoDB is entirely serverless, and you define a table’s throughput capacity at the table scope level. BigTable is unique because you can interact directly with all tables through the instances you’ve built.
The operational overhead is also affected by server-based and serverless architecture. BigTable requires infrastructure provisioning, whereas DynamoDB uses an auto-scaling feature to adapt throughput per the workload. BigTable can also be scaled, but you must write scaling parameters and trigger them in response to metrics such as CPU.
In DynamoDB, the data is stored on SSDs and replicated across multiple regional zones. Furthermore, it includes manual and automatic backups enabling Point-In-Time Recovery.
You can recover data for the previous 35 days. In the case of BigTable, you define whether you need replication or not. Backups are stored in the cluster and thus are linked to its lifecycle.
If the cluster is down, you won’t be able to access the backup. Also, you cannot export backup anywhere. On the other hand, DynamoDB keeps the most recent backup for 35 days after the table is removed.
DynamoDB supports both local and global indexes. The local index has different sort keys and a single partition key. At the same time, the Global index has other partition keys. BigTable has no support for sort keys and secondary indices. Even though it appears to be a limitation, BigTable compensates slightly with ample storage.
The maximum size of a BigTable row (recommended 100MB) is much larger than in DynamoDB (400 KB). So you can create large tables with multiple events in a row. BigTable columns are organized lexicographically into families, and the data is stored and cached together from each family.
Working with time-partitioned tables is recommended by DynamoDB documentation. BigTable, on the other hand, advises using fewer tables and avoiding creating new tables for datasets that have the same schema.
Both DynamoDB and BigTable support single-row reading using only the primary key, but as DynamoDB has a secondary key, you can add some additional criteria.
Google BigTable and Amazon DynamoDB may sound similar, but they are significantly different. Based on these differences, you can choose the one that fits your needs.