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AWS Database Services

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Amazon Redshift Serverless

2024-07-19T00:24:57+00:00

Bookmarks Use Cases Features Components Monitoring Security Pricing Amazon Redshift Serverless Cheat Sheet Amazon Redshift Serverless allows users to run and scale analytics without managing the underlying data warehouse infrastructure. It dynamically adjusts compute capacity to handle fluctuating query loads, delivering high performance and efficiency for analytical workloads. Amazon Redshift Serverless Use Cases Ideal for workloads with unpredictable usage patterns, where traditional data warehousing solutions may need to be more cost-effective and practical. Supports integration with BI tools like Tableau and Amazon QuickSight for real-time and historical data analysis. Seamlessly integrates [...]

Amazon Redshift Serverless2024-07-19T00:24:57+00:00

Amazon Aurora Machine Learning

2023-05-02T05:15:36+00:00

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 Learning2023-05-02T05:15:36+00:00

Aurora Serverless Tutorial Part 2

2023-05-27T08:37:50+00:00

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 22023-05-27T08:37:50+00:00

Aurora Serverless Tutorial – Part 1

2023-06-02T02:06:31+00:00

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 12023-06-02T02:06:31+00:00

Amazon Quantum Ledger Database (QLDB)

2023-06-23T06:29:32+00:00

Bookmarks How it Works Common Use Cases Components Of QLDB Performance Scalability Reliability Backup and Restore Security Pricing Limitations Amazon Quantum Ledger Database Cheat Sheet 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 [...]

Amazon Quantum Ledger Database (QLDB)2023-06-23T06:29:32+00:00

Amazon DocumentDB

2024-11-14T09:08:14+00:00

Bookmarks How it Works Performance Scaling Reliability Backup and Restore Security Pricing Limitations Amazon DocumentDB Cheat Sheet 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 [...]

Amazon DocumentDB2024-11-14T09:08:14+00:00

Amazon Neptune

2024-11-14T09:16:22+00:00

Bookmarks How it Works Common Use Cases Performance Reliability Backup And Restore Security Pricing Monitoring Limitations Amazon Neptune Cheat Sheet 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 [...]

Amazon Neptune2024-11-14T09:16:22+00:00

Global Secondary Index vs Local Secondary Index

2024-01-24T09:25:20+00:00

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 Local secondary index Definition An index with a partition key and a sort key that can be different from those on the base table. An index that has the same partition key as the base table, but a different sort key.  Span of query Queries on the index can span [...]

Global Secondary Index vs Local Secondary Index2024-01-24T09:25:20+00:00

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

2021-05-04T09:10:25+00:00

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

2021-05-03T13:23:37+00:00

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

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