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Amazon Macie


Amazon Macie A security service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS. Macie recognizes sensitive data such as personally identifiable information (PII) or intellectual property. Amazon Macie is currently only available in N. Virginia and Oregon region. Amazon Macie allows you to achieve the following: Identify and protect various data types, including PII, PHI, regulatory documents, API keys, and secret keys Verify compliance with automated logs that allow for instant auditing Identify changes to policies and access control lists Observe changes in user behavior and receive actionable alerts Receive notifications when data and [...]

Amazon Macie2020-01-29T00:24:01+00:00

IP Blocking: Use AWS WAF or NACL?


IP Blocking: Use AWS WAF or NACL? What should you do if you identified a series of malicious attacks on your application coming from a specific IP address? Will you use AWS WAF to block that IP address or create a rule in your Network Access Control List to deny traffic from that IP? It is true that AWS WAF can filter web requests based on IP addresses, HTTP headers, HTTP body, or URI strings, to block common attack patterns, such as SQL injection or cross-site scripting. NACL, on the other hand, acts like a firewall for controlling traffic in [...]

IP Blocking: Use AWS WAF or NACL?2019-11-20T03:06:03+00:00

How to invalidate API Gateway Cache


How to invalidate API Gateway Cache To invalidate an existing cache entry of a request and retrieve the latest data from the integration endpoint, one must send the request together with the Cache-Control: max-age=0 header. If the recipient is authorized to communicate directly to the integration endpoint, then the integration endpoint will respond with the latest data for the request. This also replaces the existing cache entry with the new response. The IAM Policy that grants a client to invalidate the cache follows: {   "Version": "2012-10-17",   "Statement": [     {       "Effect": "Allow",       "Action": [         "execute-api:InvalidateCache"       ],       "Resource": [ "arn:aws:execute-api:region:account-id:api-id/stage-name/GET/resource-path-specifier"       ]     } [...]

How to invalidate API Gateway Cache2019-11-01T12:54:25+00:00

Instrumenting your Application with AWS X-Ray


Instrumenting your Application with AWS X-Ray Instrumenting your Node.js application The AWS X-Ray SDK for Node.js provides middleware that you can use to instrument incoming HTTP requests. You need to add the SDK to your application’s dependencies, usually via package.json. Initialize the SDK client and add it to your application prior to declaring routes. var AWSXRay = require('aws-xray-sdk'); AWSXRay.setDaemonAddress('host:port'); app.use('MyApp'));       3. Lastly, use the SDK exceptions after declaring routes. app.get('/', function (req, res) {   res.render('index'); }); app.use(; Instrumenting your Java application The AWS X-Ray SDK for Java provides a servlet filter that you can add to your [...]

Instrumenting your Application with AWS X-Ray2019-10-30T00:49:55+00:00

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


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 [...]

Calculating the Required Read and Write Capacity Unit for your DynamoDB Table2019-10-29T06:43:43+00:00

AWS Lambda Integration with Amazon DynamoDB Streams


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 [...]

AWS Lambda Integration with Amazon DynamoDB Streams2019-10-29T06:51:11+00:00

Kinesis Scaling, Resharding and Parallel Processing


Kinesis Scaling, Resharding and Parallel Processing Kinesis Resharding enables you to increase or decrease the number of shards in a stream in order to adapt to changes in the rate of data flowing through the stream. Resharding is always pairwise. You cannot split into more than two shards in a single operation, and you cannot merge more than two shards in a single operation. The Kinesis Client Library (KCL) tracks the shards in the stream using an Amazon DynamoDB table, and adapts to changes in the number of shards that result from resharding. When new shards are created as a [...]

Kinesis Scaling, Resharding and Parallel Processing2019-10-29T06:59:16+00:00

DynamoDB Scan vs Query


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 [...]

DynamoDB Scan vs Query2019-10-29T07:06:04+00:00

ECS Task Placement Strategies


ECS Task Placement Strategies A task placement strategy is an algorithm for selecting instances for task placement or tasks for termination. When a task that uses the EC2 launch type is launched, Amazon ECS must determine where to place the task based on the requirements specified in the task definition, such as CPU and memory. Similarly, when you scale down the task count, Amazon ECS must determine which tasks to terminate.  A task placement constraint is a rule that is considered during task placement. You can use constraints to place tasks based on Availability Zone or instance type.  You can [...]

ECS Task Placement Strategies2019-10-29T08:37:27+00:00

AWS Serverless Application Model (SAM)


AWS Serverless Application Model (SAM) An open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings.  You create a JSON or YAML configuration template to model your applications.  During deployment, SAM transforms and expands the SAM syntax into AWS CloudFormation syntax. Any resource that you can declare in an AWS CloudFormation template you can also declare in an AWS SAM template. The SAM CLI provides a Lambda-like execution environment that lets you locally build, test, and debug applications defined by SAM templates. You can also use the SAM CLI to deploy [...]

AWS Serverless Application Model (SAM)2019-10-29T08:31:55+00:00

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