Amazon DynamoDB is a fully managed NoSQL database service offered by Amazon.com as a part of their Amazon Web Services portfolio. Many of the world’s renowned businesses and enterprises use DynamoDB to support their mission-critical workloads.
Basin is a simple form backend that allows you to collect submission data without writing a single line of code.Basin Integrations
Amazon DynamoDB + Amazon DynamoDBGet IP2Location information for IP addresses from new AWS DynamoDB items and store it in a separate table Read More...
Basin + AWeberAdd new AWeber subscribers from new form submission in Basin Read More...
Basin + Google SheetsCreate Google Sheet rows on new Basin form submissions Read More...
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Amazon DynamoDB is a fully managed NoSQL database. It is a proprietary key-value store that provides fast and predictable performance with seamless scalability. DynamoDB uses a proprietary AWS Storage System that automatically spreads data across servers, regions, and Availability Zones. Amazon DynamoDB delivers predictable, high performance for both throughput and latency-sensitive application workloads. Customers can scale the capacity of their databases by starting with a small provisioned capacity and increasing it later when they need to.DynamoDB can hold massive amounts of data—from a few terabytes to a few petabytes—and can process thousands of requests per second across multiple items with low latency.The Amazon DynamoDB web service is highly available, secure, and durable. Data is stored redundantly across multiple availability zones in an Amazon Virtual Private Cloud (VPC.Amazon DynamoDB supports the following:
Amazon DynamoDB is designed to deliver high availability (99.95 percent), throughput, and low latency at any scale. The system maintains high availability with multiple copies of data and continues to operate despite ongoing software failures or partial infrastructure failures.If a single hardware component fails, the system continues to operate using a different copy of the data. If all copies are unavailable due to concurrent failures, data is unavailable until the failed components are replaced. The system provides constant uptime by monitoring itself and automatically healing any problems that occur.The system scales automatically from gigabytes to exabytes and from a few requests per second to thousands of requests per second. Scaling up and down is transparent to applications. To change capacity or throughput capacity, you simply increase or decrease the number of read and write units that you provision through the AWS Management Console or through the Amazon DynamoDB API. By default, all tables support at least one write unit and one read unit.To reduce latency, Amazon DynamoDB stores data on multiple devices in an AWS Region. To achieve maximum throughput, data is spread across multiple partitions on multiple devices in the same Availability Zone. Each partition contains multiple items.As read and write units increase, so does the number of partitions that hold data for a table. Amazon DynamoDB distributes items evenly among partitions to ensure even distribution of data across partitions in the same table.If your application needs more throughput than what is currently provisioned, Amazon DynamoDB can distribute additional partitions throughout your VPC so that you can handle more traffic without changing the number of read and write units currently provisioned for your table. If you don't add extra partitions, Amazon DynamoDB creates them automatically when you increase the number of read and write units for a table. This ensures that your application always has access to sufficient read and write capacity in every partition in the table.If a table requires more throughput than what is possible with a single table, you can create multiple tables to increase your total throughput capacity. You can give each table a unique name so that you can easily identify the intended purpose of each table. When you create multiple tables, you must also provision enough read and write capacity to ensure that each table’s combined throughput meets your application’s requirements. For example, if you have two tables named Table1 and Table2 each with two read units and four write units, you must provision at least six read units and eight write units so that all tables can handle the expected amount of load.In addition to creating multiple tables in a single account, you can also create multiple accounts across multiple regions within your AWS account to help improve availability and fault tolerance. For example, if you create an account in US West (Oregon. with three tables named Table1, Table2, and Table3 in each region, you must also provision at least nine read units and twelve write units in each region to ensure that your application has sufficient read and write capacity across all tables in all regions.With Amazon DynamoDB, customers pay only for the throughput capacity they use. You don't pay for unused capacity, which makes this pricing model ideal for cost-sensitive applications where unexpected growth may occur due to viral effects or word-of-mouth referrals. Furthermore, storage costs are consistent regardless of how many items are stored or how much data is stored per item. You only pay for the throughput capacity that your application uses—there are no upfront fees or long-term contracts required to get started with Amazon DynamoDB. You pay as you go!Amazon DynamoDB supports put/get/delete operations over HTTP or HTTPS using REST or Query APIs. You can use either API to perform administrative tasks such as creating tables and adding items to secondary indexes or setting permissions on individual items or groups of items within a table.Finally, Amazon DynamoDB integrates with other AWS services to extend its functionality. Amazon S3 for storage of large binary objects; Amazon EMR for running distributed batch computations; Amazon Redshift for working with large amounts of data; Amazon Machine Learning for training machine-learning classifiers; and Amazon Kinesis for real-time streaming data analysis.How does Amazon DynamoDB compare to Google Bigtable?Google Bigtable is similar to Amazon DynamoDB in that it is a fully managed NoSQL database with auto-scaling capabilities based on demand rather than pre-provisioning capacity like traditional relational databases. Like Amazon DynamoDB, Google Bigtable automatically spreads data across servers, regions, and Availability Zones to provide high availability and durability while reducing latency caused by contention on limited compute resources. Each Google Bigtable instance consists of one master node and many slave nodes that replicate data from the master node and provide fault tolerance in case of failures in master nodes or datacenter outages. When new slave nodes are added, they automatically replicate data from the master node within seconds without requiring human intervention to repopulate the newly created slave nodes with data from the master node.Google Bigtable was designed for storing large amounts of unstructured data such as web logs or sensor data generated by Internet of Things (IoT. devices without requiring schema predefinition or indexing before storing data into Bigtable’s columnar format. However, unlike Bigtable’s flat schema design where columns are stored together on the same row in a single column family, Amazon DynamoDB uses a nested schema design that allows storing multiple columns together on the same row in multiple column families within a single hash key range partition on the primary partition key attribute value pair (or primary key.In addition to storing data within hash key range partitions on primary partition key attribute value pairs (or primary keys), you can also store additional related information within secondary index range partitions on secondary index attribute value pairs (or secondary keys. Secondary index range partitions are optional attributes that can be attached to primary partition key attribute value pairs (or primary keys. within hash key range partitions to facilitate searching for data within a hash key range partition based on values within a single primary partition key attribute value pair (or primary key. If no secondary indexes exist, you can search for specific primary partition key attribute values by scanning a hash key range partition sequentially from start to end looking for matches on the primary partition key attribute value pair (or primary key.You can attach up to 10 secondary indexes to every hash key range partition containing up to 1 billion items per partition. Each secondary index range partition contains up to 500 terabytes of data by default although this limit can be increased if needed by contacting AWS Support regarding increasing the maximum size of a secondary index range partition container up to 50 terabytes at no additional charge beyond normal usage charges on storage in Amazon Simple Storage Service (Amazon S3. for data retained beyond 30 days . Secondary indexes allow you to search for specific values within specific ranges within a hash key range partition based on values within each secondary index attribute value pair (or secondary key. Secondary indexes do not affect throughput performance like full table scans would since they are used only when looking up specific values within specific ranges within specified hash key range partitions based on values within each secondary index attribute value pair (or secondary key.An advantage of Google Bigtable is its ability to store and query structured data stored as JSON objects within a column family within each hash key range partition instead of just unstructured blobs of binary data like Amazon DynamoDB which doesn’t support storing or querying structured data stored as JSON objects within column families within each hash key range partition . In addition to storing unstructured blobs of binary data like Amazon DynamoDB , Google Bigtable also supports storing semi-structured objects like JSON objects which is useful when storing semi-structured business data containing multi-dimensional arrays used frequently by developers working on large-scale enterprise applications involving IoT devices generating large volumes of semi-structured business data that needs processing using MapReduce queries or Spark SQL queries during real-time analytics scenarios .Google
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