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.
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Amazon DynamoDB + Amazon DynamoDBGet IP2Location information for IP addresses from new AWS DynamoDB items and store it in a separate table Read More...
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Amazon DynamoDB is a fully managed cloud based NoSQL database service that utilizes a flexible data model to store and retrieve any amount of data, while providing consistent low latency regardless of the request volume.Amazon DynamoDB provides a global distributed data store with seamless scalability, high availability, and strong consistency with Amazon Web Services. With scale capacity, you can create tables with more than 100 TB of storage, and provision throughput of more than 1 million requests per second.Amazon DynamoDB is an on-demand database that is available 24/7 and can be accessed from anywhere in the world.To get started with Amazon DynamoDB, you can sign up for a free tier and run your first table in minutes. You can then immediately start using the AWS SDKs or one of their local development tools to access your data.You can also use Amazon DynamoDB through a web-based console that lets you easily create tables, execute queries, and perform other management tasks.
Timely by Memory is a solution that enables businesses to connect existing enterprise data sources to Amazon DynamoDB in real time. As a result, they can perform analysis on large, high-volume datasets without incurring prohibitive infrastructure costs.Timely by Memory allows users to interact with data stored in Amazon DynamoDB from their existing enterprise systems. With Timely by Memory, users are able to ingest data from various external sources and aggregate it in real time into the canonical source of truth within Amazon DynamoDB. Timely by Memory provides its users with an easy-to-use service that offers both flexibility and functionality. For instance, the solution supports different types of data models, including relational, key-value, graph, and document style. Users can also choose from a variety of connectors, including Tableau, Oracle, SQL Server, MongoDB, ElasticSearch and more.Timely by Memory offers users with an easy way to access large amounts of data stored within Amazon DynamoDB through the Timely by Memory Query Language (TMQL. TMQL is an open source language that is used to query hundreds of millions of rows per second. TMQL has many advantages over SQL including the ability to process nested JSON documents and JSON arrays natively as well as support for new Amazon DynamoDB features such as LocalSecondaryIndexes and GlobalSecondaryIndexes. TMQL is also easy to learn for those who are familiar with SQL because it uses similar concepts such as joins and subqueries. Additionally, the solution provides users with control over which partitions they want to query and whether they want to query across multiple partitions simultaneously.Timely by Memory also comes with a comprehensive monitoring dashboard that gives users insight into how their application is performing. The dashboard presents a number of useful metrics including the number of records processed by an instance as well as the overall throughput for all instances. The solution also includes a series of charts that track metrics such as the average response time per partition and the batch size over time.Timely by Memory integrates seamlessly with AWS Lambda and Amazon Kinesis Streams to capture all of the incoming data and pass it onto Amazon S3 buckets or stream it into Amazon Kinesis Streams for further processing.
Integrating Amazon DynamoDB with Timely by Memory is a straightforward process that does not require any code changes on the part of the user. First, users will need to install Node.js on their machine as well as the Timely by Memory command line tool ( tbmm . After installing Node.js and the command line tool, users can begin creating their connector configuration file ( JSON . using the tbmm init command. This command will generate a basic JSON file for them containing some default values which they can change if necessary. Next, users can use the tbmm update command to update the connector configuration file with their AWS credentials so that they can authenticate with AWS. Once this is complete, users can create a database connection using the tbmm create-db-connection command. They can then use this newly created connection to integrate Amazon DynamoDB with Timely by Memory using the tbmm feed command. The final step is to run their application against the newly configured database connection using the tbmm feed command followed by either the run or deploy command depending on whether they are running or deploying their application respectively.Finally, users can take advantage of TMQL queries in combination with Timely by Memory's ability to query across multiple partitions to analyze large amounts of data from multiple sources in real time within Amazon DynamoDB. For example, suppose a company has an eCommerce website that sells products online and collects customer feedback through email surveys every month. In order to analyze this customer feedback in real time and identify trends for future marketing campaigns, they may wish to store their survey responses in Amazon S3 temporarily until they have enough data to run TMQL queries against it in Amazon DynamoDB without incurring too many costs as they might if they were querying directly against Amazon S3 from their application servers. B. Benefits of Integration of Amazon DynamoDB and Timely by Memory
The integration of Amazon DynamoDB with Timely by Memory offers various benefits to users including:Simple Setup – Users can get up and running quickly thanks to the simplicity of setting up and configuring both solutions together. High Performance – TMQL is an open source language that is capable of querying hundreds of millions of rows per second in Amazon DynamoDB due to its ability to process nested JSON documents and JSON arrays natively as well as support for new features such as LocalSecondaryIndexes and GlobalSecondaryIndexes introduced within Amazon DynamoDB . Flexible Data Model – Users can choose from different data models including relational, key value, graph, and document style when storing their data within Timely by Memory . This helps them adapt to changing business needs more easily since they do not need to make any code changes when switching between different data models . Integration with Other Tools – Integration with other tools provided by AWS as well as third-party tools such as Tableau provides users with greater flexibility when performing analysis on their data within Amazon DynamoDB .
Now that we have taken a look at what Amazon DynamoDB and Timely by Memory are and how they are used together, we can see how powerful this combination is for analyzing large amounts of data in real time within Amazon DynamoDB from different sources while maintaining low costs due to efficient resource utilization.
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