Database

The DynamoDB Scan API accesses every items in a table (or secondary index). It is the equivalent of a select * from query. One of the things I will cover in this blog is how to use Scan API with the DynamoDB Go SDK.

To scan a table, we need some data to begin with! So in the process, I will also go into how to use the Batch API to write bulk data in DynamoDB. You can use the BatchWriteItem API to create or delete items in batches (of twenty five) and it’s possible to you can combine these operations across multiple tables.

Learn with practical code samples

DynamoDB provides a rich set of data types including Strings, Numbers, Sets, Lists, Maps etc. In the Go SDK for DynamoDB, the types package contains Go representations of these data types and the attributevalue module provides functions to work with Go and DynamoDB types.

This blog post will demonstrate how to handle conversions between Go types in your application and DynamoDB. We will start off with simple code snippets to introduce some of the API constructs and wrap up with a example of how to use these Go SDK features in the context of a complete application (including a code walk though).

Use change data capture with MSK Connect to sync data between Aurora MySQL and DynamoDB

This is the second part of the blog series which provides a step-by-step walkthrough of data pipelines with Kafka and Kafka Connect. I will be using AWS for demonstration purposes, but the concepts apply to any equivalent options (e.g. running these locally in Docker).

This part will show Change Data Capture in action that let’s you track row-level changes in database tables in response to create, update and delete operations. For example, in MySQL, these change data events are exposed via the MySQL binary log (binlog).

This is the second blog post of this two-part series that uses a practical application to demonstrate how to integrate Redis with AWS Lambda. The first part was about the solution overview, deployment and hopefully you were able to try it out end to end. As promised, the second part (this one) will cover the Infrastructure aspects (IaaC to be specific) which is comprised of three (CDK) Stacks (in the context of a single CDK App).

Run your Go app on AWS App Runner service, integrate it with MemoryDB for Redis and use AWS CDK to package and deploy the app along with infrastructure

AWS App Runner allows you to deploy and run cloud-native applications in a fast, simple, and cost-effective manner. You can choose the programming language of your choice since App Runner can deploy directly from source code (in GitHub for example) or a Docker container image (from private or public repo in ECR) - all this without worrying about provisioning and managing the underlying infrastructure.

Hello and welcome 👋🏼 to this two-part blog series that uses a practical application to demonstrate how to integrate Redis with AWS Lambda. The first part (this one) covers the application - by the end of this blog, you should have the solution deployed, played around with it and in the process, have a good overview of the solution.

The second part is about the Infrastructure (IaaC to be specific) aspects - its mostly centred around AWS CDK along with some code walkthrough.

This quick-start uses AWS Cloud9 IDE to help you get up and running with MemoryDB for Redis, quickly

When I was initially exploring some of the AWS services (such as MemoryDB, MSK, Elasticache for Redis etc.), I usually followed the documentation that involved setting up EC2, SSH-ing into the instance, install/copy stuff (language, runtime, code, client etc.) and then try things out. Most often, the first step is the hardest, and it’s important for developers to have the least amount of friction as possible to “get going”.

Originally posted on https://devblogs.microsoft.com/cosmosdb/partial-document-update-ga/

We’re excited to announce the general availability of partial document update for the Azure Cosmos DB Core (SQL) API, which was announced at Microsoft Ignite! This new feature makes it possible to perform path-level updates to specific fields/properties in a single document without needing to perform a full document read-replace operation. Partial document update is currently supported in Azure Cosmos DB .NET SDK, Java SDK, Node SDK, and stored procedures.

Document updates – then and now

To put things in context, here is a refresher on how one would typically use a document replace operation: Read the document, update it locally (client side) including any optimistic concurrency control (OCC) checks if necessary and, finally call the replace operation along with the updated document.

Azure Cosmos DB is a fully managed, elastically scalable and globally distributed database with a multi-model approach, and provides you with the ability to use document, key-value, wide-column, or graph-based data.

We will drill further into the multi-model capabilities and explore the options that are available to store and access data. Hopefully, it can help you make an informed decision on the right API are the right choice.

• Core (SQL) API: Flexibility of a NoSQL document store combined with the power of SQL for querying.
• MongoDB API: Supports the MongoDB wire protocol so that existing MongoDB client continue to work with Azure Cosmos DB as if they are running against an actual MongoDB database.
• Cassandra API: Supports the Cassandra wire protocol so that existing Apache drivers compliant with CQLv4 continue to work with Azure Cosmos DB as if they are running against an actual Cassandra database.
• Gremlin API: Supports graph data with Apache TinkerPop (a graph computing framework) and the Gremlin query language.
• Table API: Provides premium capabilities for applications written for Azure Table storage.

You can read further on the some of the Key Benefits

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