In today’s data-driven world, effective data management is crucial for organizations aiming to make well-informed, data-driven decisions. As the importance of data continues to grow, so does the significance of robust data management practices. This includes the processes of ingesting, storing, organizing, and maintaining the data generated and collected by an organization. Within the realm of data management, schema evolution stands out as one of the most critical aspects. Businesses evolve over time, leading to changes in data and, consequently, changes in corresponding schemas. Even though a schema may be initially defined for your data, evolving business requirements inevitably demand schema modifications. Yet, modifying data structures is no straightforward task, especially when dealing with distributed systems and teams. It’s essential that downstream consumers of the data can seamlessly adapt to new schemas. Coordinating these changes becomes a critical challenge to minimize downtime and prevent production issues. Neglecting robust data management and schema evolution strategies can result in service disruptions, breaking data pipelines, and incurring significant future costs. In the context of Apache Kafka, schema evolution is managed through a schema registry. As producers share data with consumers via Kafka, the schema is stored in this registry. The Schema Registry enhances the reliability, flexibility, and scalability of systems and applications by providing a standardized approach to manage and validate schemas used by both producers and consumers. This blog post will walk you through the steps of utilizing Amazon MSK in combination with AWS Glue Schema Registry and Terraform to build a cross-account streaming pipeline for Kafka, complete with built-in schema evolution. This approach provides a comprehensive solution to address your dynamic and evolving data requirements.
Articles tagged with "level-400"
Der Amazon OpenSearch Service, der auf dem robusten OpenSearch-Framework basiert, zeichnet sich durch seine bemerkenswerte Geschwindigkeit und Effizienz in Such- und Analysefunktionen aus. Trotz seiner Stärken sind die Standardkonfigurationen des Dienstes möglicherweise nicht vollständig darauf ausgelegt, die spezifischen sprachlichen Herausforderungen bestimmter Sprachen zu bewältigen.
Welcome back to our series on implementing SAML Federation for Amazon OpenSearch Service. In our previous post, we explored setting up SAML Federation using OneLogin. Today, we’ll focus on another popular identity provider - Keycloak. Keycloak is an open-source Identity and Access Management solution, ideal for modern applications and services. We’ll guide you through integrating Keycloak with Amazon OpenSearch Service to implement SAML Federation.
Amazon OpenSearch Service, utilizing the robust OpenSearch framework, excels in search and analytics due to its remarkable speed and efficiency. Despite its strengths, the service’s default configurations might not be fully tailored to address the distinct linguistic challenges encountered in specific languages. Take German, for example, known for its compound words like “Lebensversicherungsgesellschaft” (life insurance company). Standard tokenization in search technologies treats these compounds as single units, leading to less optimal search results. For improved accuracy, it’s important to index the components of these compounds separately – “Leben” (life), “Versicherung” (insurance), and “Gesellschaft” (company). This approach ensures more precise and effective search outcomes, particularly in languages like German with many compound words.
In the process of constructing your Hybrid Hub and Spoke Network within the Cloud, which includes the integration of On-Premises networks and allows internet-based access, the implementation of a network firewall is essential for robust security. This security measure involves thorough traffic analysis and filtering between the entities to safeguard against both internal and external cyber threats and exploits. By actively monitoring and inspecting the flow of traffic, a network firewall plays a crucial role in identifying and blocking vulnerability exploits and unauthorized access attempts. Within the AWS ecosystem, the AWS Network Firewall is a service that is often used for achieving a high level of network security. As a stateful and fully managed network firewall, it includes intrusion detection and prevention capabilities, offering comprehensive protection for VPC-based network traffic. This blog post aims to guide you through the process of integrating the AWS Network Firewall into your hybrid AWS Hub and Spoke network. By doing so, you can effectively analyze, monitor, and filter both incoming and outgoing network traffic among all involved parties, thereby enhancing the overall security of your infrastructure layer.
When leveraging AWS services such as EC2, ECS, or EKS, achieving standardized and automated image creation and configuration is essential for securely managing workloads at scale. The concept of a Golden AMI is often used in this context. Golden AMIs represent pre-configured, hardened and thoroughly tested machine images that encompass a fully configured operating system, essential software packages, and customizations tailored for specific workload. It is also strongly recommended to conduct comprehensive security scans during the image creation process to mitigate the risk of vulnerabilities. By adopting Golden AMIs, you can ensure consitent configuration across different environments, leading to decreased setup and deployment times, fewer configuration errors, and a diminished risk of security breaches. In this blog post, I would like to demonstrate how you can leverage AWS CodePipeline and AWS Stepfunctions, along with Terraform and Packer, to establish a fully automated pipeline for creating Golden AMIs.
In this blog post, we’ll explore how you can teach the DynamoDB Table resource in boto3 (and the client) to store and retrieve Python’s datetime and float objects, which they can’t do natively. We’ll also discuss why you should or shouldn’t do that.
When implementing a hybrid cloud solution and connecting your AWS VPCs with corporate data centers, setting up proper DNS resolution across the whole network is an important step to ensure full integration and functionality. In order to accomplish this task, Route53 Inbound and Outbound endpoints can be used. In combination with forwarding rules, they allow you to forward DNS traffic between your AWS VPC and on-premises data centers. In this blog post, I would like to show you how you can leverage Route53 endpoints in combination with Terraform to establish seamless DNS query resolution across your entire hybrid network.