AWS Database Services

//AWS Database Services

Amazon Aurora Machine Learning


Amazon Aurora Machine Learning is a proprietary technology of Amazon that enables a native SQL user to integrate Machine Learning-based predictions to an application without knowing or understanding any machine learning algorithms. Machine learning heavily relies on datasets for it to work. You can say that data is the oil that keeps the engine of machine learning running. There is a massive amount of data generated every day. To give you an idea, according to this article, “By 2020, it’s estimated that for every person on earth, 1.7 MB of data will be created every second.”  Almost 2 MB of [...]

Amazon Aurora Machine Learning2021-05-04T09:09:24+00:00

Aurora Serverless Tutorial Part 2


In the first part of this tutorial, we gave a walkthrough on Aurora Serverless and its use case. You can read the article here. For this tutorial, we will do some hands-on training and create an Aurora Serverless database. Creating an Aurora Serverless Database: 1. Open the AWS console and go to RDS. Click the button “Create database”. 2. Choose Amazon Aurora. You can either choose MySQL or PostgreSQL compatibility. In this tutorial, we will use MySQL compatibility. As of this writing, there are two versions of MySQL that Aurora serverless supports. Under the Database Features, select serverless. 3. Under [...]

Aurora Serverless Tutorial Part 22021-05-03T13:22:18+00:00

Aurora Serverless Tutorial – Part 1


What is Aurora Serverless?  Before we get into it, let us briefly define Aurora and serverless first. Aurora is a fully managed, closed source relational database that is compatible with MySQL and PostgreSQL. According to Amazon, it is five times faster than the standard MySQL and three times faster than PostgreSQL. It uses a distributed architecture that provides fault tolerance and high availability.  Serverless is a technique in the cloud that follows the ‘pay-per-use’ model. As opposed to its name, serverless does not mean not using ‘servers’. There is no magic in it. It still uses a physical server that [...]

Aurora Serverless Tutorial – Part 12021-05-03T13:22:11+00:00

Amazon Quantum Ledger Database (QLDB)


Bookmarks How it Works Common Use Cases Components Of QLDB Performance Scalability Reliability Backup and Restore Security Pricing Limitations Fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log ‎owned by a central trusted authority. Used to track all application data changes, and maintain a complete and verifiable history of changes over time Amazon QLDB is serverless.  No capacity provisioning required or setting read/write limits. QLDB transactions are ACID (atomicity, consistency, isolation, and durability) compliant. Amazon QLDB uses PartiQL as its query language. [...]

Amazon Quantum Ledger Database (QLDB)2021-05-11T08:43:40+00:00

Amazon DocumentDB


Bookmarks How it Works Performance Scaling Reliability Backup and Restore Security Pricing Limitations Fully managed document database service designed to be fast, scalable, and highly available. Data is stored in JSON-like documents. Compatible with MongoDb. Flexible schema and indexing. Commonly used for content management, user profiles, and real-time big data. How it Works   An Amazon DocumentDB cluster decouples storage and compute. A cluster consists of Cluster volume and Instances Cluster volume refers to the storage layer that spans multiple Availability Zones. Each Availability Zone has a copy of [...]

Amazon DocumentDB2022-07-16T05:55:31+00:00

Amazon Neptune


Bookmarks How it Works Common Use Cases Performance Reliability Backup And Restore Security Pricing Monitoring Limitations Amazon Neptune is a fully managed graph database service used for building applications that work with highly connected datasets. Optimized for storing billions of relationships between pieces of information. Provide milliseconds latency when querying the graph. Neptune supports graph query languages like Apache TinkerPop Gremlin and W3C's SPARQL. How it works Common Use Cases Social Networking Amazon Neptune can easily process user’s interactions like comments, follows, and likes in a social network [...]

Amazon Neptune2021-06-16T02:12:11+00:00

Global Secondary Index vs Local Secondary Index


Bookmarks Global Secondary Index Local Secondary Index A secondary index is a data structure that contains a subset of attributes from a table, along with an alternate key to support Query operations. An Amazon DynamoDB table can have multiple secondary indexes. Global Secondary Index Read/Write Capacity Calculation (Provisioned Throughput Mode) Eventually consistent reads consume ½ read capacity unit. Therefore, each query can retrieve up to 8KB of data per capacity unit (4KB x 2). The maximum size of the results returned by a Query operation is 1 MB. The total provisioned throughput cost for a [...]

Global Secondary Index vs Local Secondary Index2021-05-06T09:40:09+00:00

Calculating the Required Read and Write Capacity Unit for your DynamoDB Table


Read Capacity Unit On-Demand Mode When you choose on-demand mode, DynamoDB instantly accommodates your workloads as they ramp up or down to any previously reached traffic level. If a workload’s traffic level hits a new peak, DynamoDB adapts rapidly to accommodate the workload. The request rate is only limited by the DynamoDB throughput default table limits, but it can be raised upon request. For on-demand mode tables, you don't need to specify how much read throughput you expect your application to perform. DynamoDB charges you for the reads that your application performs on your tables in terms of read request [...]

Calculating the Required Read and Write Capacity Unit for your DynamoDB Table2021-05-04T09:10:25+00:00

AWS Lambda Integration with Amazon DynamoDB Streams


Amazon DynamoDB is integrated with AWS Lambda so that you can create triggers, which are pieces of code that automatically respond to events in DynamoDB Streams. With triggers, you can build applications that react to data modifications in DynamoDB tables. After you enable DynamoDB Streams on a table, associate the DynamoDB table with a Lambda function. AWS Lambda polls the stream and invokes your Lambda function synchronously when it detects new stream records.  Configure the StreamSpecification you want for your DynamoDB Streams: StreamEnabled (Boolean) - indicates whether DynamoDB Streams is enabled (true) or disabled (false) on the table. StreamViewType (string) [...]

AWS Lambda Integration with Amazon DynamoDB Streams2021-05-03T13:23:37+00:00

DynamoDB Scan vs Query


Scan The Scan operation returns one or more items and item attributes by accessing every item in a table or a secondary index. The total number of scanned items has a maximum size limit of 1 MB. Scan operations proceed sequentially; however, for faster performance on a large table or secondary index, applications can request a parallel Scan operation. Scan uses eventually consistent reads when accessing the data in a table; therefore, the result set might not include the changes to data in the table immediately before the operation began. If you need a consistent copy of the data, as [...]

DynamoDB Scan vs Query2021-05-03T13:22:24+00:00

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