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AWS Step Functions announces an optimized integration for AWS Elemental MediaConvert, enabling video transcode jobs in your workflow. With this integration, customers can easily build automated, resilient media workflows using the visual authoring and operator experience of Step Functions. With the new Run a Job (.sync) integration pattern for the MediaConvert CreateJob API, Step Functions will wait for asynchronous MediaConvert transcoding jobs to complete before progressing to the next step, simplifying the orchestration of multi-step media processing pipelines. View the full article

In today’s fast-paced world, businesses always seek opportunities to expand operations using automated tools and platforms. Adroll and Salesforce are effective cloud-based platforms that help you market your products and services with advanced functions and algorithms. Adroll optimizes your ads through different channels, while Salesforce’s data-driven tools help you manage your workflow and enhance your […]View the full article

Data migration between different platforms is critical to consider when generating multiple organizational strategies. One such migration involves transferring data from a cloud-based data warehousing service environment to a relational database management system. Amazon Redshift SQL Server Integration can give organizations an edge, allowing them to conduct enhanced analysis and reporting through custom report application […]View the full article

Managing databases is an essential part of business. It helps you store and retrieve data on the go. With the latest technologies in the market, deciding which database management system is the best has become tough. One such necessary factor is the cost considerations of choosing a database. MySQL is a free database management system […]View the full article

Maximizing the effectiveness of proper marketing attempts is necessary while ensuring a flawless customer experience. Corporations prioritize focusing on the key performance indicators (KPIs) that positively influence their business by integrating their in-house data with marketing platforms. In this dynamic environment, it becomes essential to connect the advertising platform to the e-commerce solution to leverage […]View the full article

Amazon DataZone is used by customers to catalog, discover, analyze, share, and govern data at scale across organizational boundaries with governance and access controls. Today, Amazon DataZone has introduced an integration with AWS Lake Formation hybrid mode. This integration enables customers to easily publish and share their AWS Glue tables through Amazon DataZone, without the need to register them in AWS Lake Formation first. Hybrid mode allows customers to start managing permissions on their AWS Glue tables through AWS Lake Formation, while continuing to maintain any existing IAM permissions on these tables. View the full article

Amazon Aurora MySQL zero-ETL integration with Amazon Redshift is now supported in 11 additional regions, enabling near real-time analytics and machine learning (ML) using Amazon Redshift. Based on your analytics needs, you can include or exclude specific databases and tables from an existing or a new zero-ETL integration and selectively bring data into Amazon Redshift. View the full article

Explore how Akeyless Vaultless Secrets Management integrates with the Kubernetes Secrets Store CSI Driver to enhance security and streamline secrets management in your Kubernetes environment. The post Enhancing Kubernetes Secrets Management with Akeyless and CSI Driver Integration appeared first on Akeyless. The post Enhancing Kubernetes Secrets Management with Akeyless and CSI Driver Integration appeared first on Security Boulevard. View the full article

In a modern, rapidly changing world, technology is fast-growing and it is pushing the progress of humanity in a whole new direction. One of the most striking technology trends in the past year is the integration of Artificial Intelligence (AI) and the Internet of Things (IoT). The two champions of the tech revolution are AI and Internet of Things and the combination of them has resulted in smart systems that are much more sophisticated and creative. Integration of AI in IoT systems AI integration into the IoT system maximizes the potential of the smart devices. The joining of AI to the big network of interrelated devices in the Internet of Things allows us to create intelligent systems that will act and think like a human being. The AI technology drives these devices and they can instantly analyze and interpret data, producing useful information that makes them more efficient and productive. IoT machine learning allows devices to learn from data, and improve their performance without hard coding. This makes IoT devices more personalized and practical since they learn and improve their performance according to the actions and preferences of people who use them. A typical example is a smart speaker that can recognize and respond to various voices in a family thus giving a special experience to every user. One more essential feature of AI IoT is predictive analytics, when AI algorithms are applied for big data analysis of IoT devices, to find patterns and anomalies. The process of prediction in the situations where possible problems or failures occur let companies make proactive steps to prevent them which leads to saving time and loss of resources of businesses. When it is so, AI IoT predictive maintenance in the manufacturing industry can cut downtime and prolong the life of the equipment. In addition, AI IoT also facilitates the improvement of business decision-making as it provides real-time data and insights. This is particularly beneficial in sectors such as retail where data from IoT devices is employed in consumer behavior monitoring and analysis, a process that leads to improved marketing initiatives and greater sales. AI IoT could also play a role in supply chain optimization by ensuring an optimal stock level and route optimization enhancing cost efficiencies. However, AI integration into IoT systems comes with its share of issues. Privacy and security gees are the biggest concern. Because of the volumes of data that are collected and processed, there is the danger of data leaks and personal data misuse. The security protocols should be adhered to by organizations so that sensitive information is protected. In addition, the AI IoT application may be difficult and resource intensive to implement. These are the smaller enterprises or the people who would want to embrace this technology limitation. Moreover, rapid changes in technology may lead to compatibility issues between different devices and platforms, which would hinder the integration of AI and Internet of Things. The integration of AI & IoT has shown success in many industries in spite of these difficulties. The pace of technological development allows AI IoT to be utilized in more spectacular ways within a short period of time. Thus, the synergy and symbiosis is dynamic, and in this situation both companies and individuals must realize the dynamic symbiosis and find the possible ways to integrate them into their lives. Benefits of AIoT The fusion of AI and Internet of Things has huge advantages for both business and individual clients. Some of the key advantages include: 1. Increased efficiency and productivity: The automation of processes and AI for data analysis, makes IoT devices run faster and more efficiently creating a productive environment. 2. Cost savings: smart IoT devices improve energy management, cut maintenance costs, and prolong the life of machines, and this leads to savings for both business and individual users. 3. Improved decision-making: AI-driven IoT equipment allows companies to gather and process live data that is essential for the making of pertinent decisions. 4. Personalization: AIoT allows devices to learn from the activities and preferences of users, providing users with personalized experiences. Challenges of AIoT The AI and Internet of Things mix have its own set of challenges despite the advantages it offers. Some of the key challenges include: 1. Data privacy and security: Big data means that touch data is collected and analyzed making it a danger of a data leak and privacy concerns. Organizations have to ensure that the given security systems are implemented so that the sensitive data is secured. 2. Complex implementation: Internet of Things AI integration task is unaffordable for most companies due to the enormous resource and human requirements. 3. Compatibility issues: The pace of technological changes is rapid and it is rather hard to combine AI and IoT, since devices and platforms remain incompatible. An overview of real-world AI and IoT integration success stories Despite challenges, some organizations were able to deploy AI and Internet of Things to develop innovative solutions. Tesla represents how self-driving features powered by AI are merged with IoT sensors that create a smart, safer, and more efficient driving process. This combination enables Tesla vehicles to process real-time data and react to the change in road conditions which lowers the probability of accidents. The health industry is an example of successful implementation of AI and Internet of Things. Smart pills from companies such as Proteus and WellDoc, on the other hand, are equipped with sensors and AI algorithms to collect patient health data to provide personalized treatment prescriptions and reminders. Future Perspectives of AIoT The IoT artificial intelligence would revolutionize many sectors such as healthcare, transportation, manufacturing and agriculture, among others. As the number of IoT devices grows exponentially and AI capabilities can do more, we expect more advanced and sophisticated applications in the future. For example, smart homes integrated with AIoT can be a fully connected and automated living environment, and in the healthcare industry, AIoT will make patient care and disease management better. The integration of AI and Internet of Things is changing the mode of interaction with technology and our environment. Moreover, it is capable of improving efficiency, productivity, and decision-making and offers a personalized experience for users. However, it also has its own challenges which need to be dealt with for it to be successfully implemented. The rapid progress of the technology will take AIoT to yet another level in the future, hence it is important to engage artificial intelligence consultancy for your daily operations and understand how you can integrate the two for your efficient operations. The post The Integration of AI and IoT: Enhancing Smart Systems appeared first on DevOpsSchool.com. View the full article

Amazon Relational Database Service (Amazon RDS) for MySQL zero-ETL integration with Amazon Redshift was announced in preview at AWS re:Invent 2023 for Amazon RDS for MySQL version 8.0.28 or higher. In this post, we provide step-by-step guidance on how to get started with near real-time operational analytics using this feature. This post is a continuation of the zero-ETL series that started with Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift. Challenges Customers across industries today are looking to use data to their competitive advantage and increase revenue and customer engagement by implementing near real time analytics use cases like personalization strategies, fraud detection, inventory monitoring, and many more. There are two broad approaches to analyzing operational data for these use cases: Analyze the data in-place in the operational database (such as read replicas, federated query, and analytics accelerators) Move the data to a data store optimized for running use case-specific queries such as a data warehouse The zero-ETL integration is focused on simplifying the latter approach. The extract, transform, and load (ETL) process has been a common pattern for moving data from an operational database to an analytics data warehouse. ELT is where the extracted data is loaded as is into the target first and then transformed. ETL and ELT pipelines can be expensive to build and complex to manage. With multiple touchpoints, intermittent errors in ETL and ELT pipelines can lead to long delays, leaving data warehouse applications with stale or missing data, further leading to missed business opportunities. Alternatively, solutions that analyze data in-place may work great for accelerating queries on a single database, but such solutions aren’t able to aggregate data from multiple operational databases for customers that need to run unified analytics. Zero-ETL Unlike the traditional systems where data is siloed in one database and the user has to make a trade-off between unified analysis and performance, data engineers can now replicate data from multiple RDS for MySQL databases into a single Redshift data warehouse to derive holistic insights across many applications or partitions. Updates in transactional databases are automatically and continuously propagated to Amazon Redshift so data engineers have the most recent information in near real time. There is no infrastructure to manage and the integration can automatically scale up and down based on the data volume. At AWS, we have been making steady progress towards bringing our zero-ETL vision to life. The following sources are currently supported for zero-ETL integrations: Amazon Aurora MySQL-Compatible Edition (generally available) Amazon Aurora PostgreSQL-Compatible Edition (preview) Amazon RDS for MySQL (preview) Amazon DynamoDB (limited preview) When you create a zero-ETL integration for Amazon Redshift, you continue to pay for underlying source database and target Redshift database usage. Refer to Zero-ETL integration costs (Preview) for further details. With zero-ETL integration with Amazon Redshift, the integration replicates data from the source database into the target data warehouse. The data becomes available in Amazon Redshift within seconds, allowing you to use the analytics features of Amazon Redshift and capabilities like data sharing, workload optimization autonomics, concurrency scaling, machine learning, and many more. You can continue with your transaction processing on Amazon RDS or Amazon Aurora while simultaneously using Amazon Redshift for analytics workloads such as reporting and dashboards. The following diagram illustrates this architecture. Solution overview Let’s consider TICKIT, a fictional website where users buy and sell tickets online for sporting events, shows, and concerts. The transactional data from this website is loaded into an Amazon RDS for MySQL 8.0.28 (or higher version) database. The company’s business analysts want to generate metrics to identify ticket movement over time, success rates for sellers, and the best-selling events, venues, and seasons. They would like to get these metrics in near real time using a zero-ETL integration. The integration is set up between Amazon RDS for MySQL (source) and Amazon Redshift (destination). The transactional data from the source gets refreshed in near real time on the destination, which processes analytical queries. You can use either the serverless option or an encrypted RA3 cluster for Amazon Redshift. For this post, we use a provisioned RDS database and a Redshift provisioned data warehouse. The following diagram illustrates the high-level architecture. The following are the steps needed to set up zero-ETL integration. These steps can be done automatically by the zero-ETL wizard, but you will require a restart if the wizard changes the setting for Amazon RDS or Amazon Redshift. You could do these steps manually, if not already configured, and perform the restarts at your convenience. For the complete getting started guides, refer to Working with Amazon RDS zero-ETL integrations with Amazon Redshift (preview) and Working with zero-ETL integrations. Configure the RDS for MySQL source with a custom DB parameter group. Configure the Redshift cluster to enable case-sensitive identifiers. Configure the required permissions. Create the zero-ETL integration. Create a database from the integration in Amazon Redshift. Configure the RDS for MySQL source with a customized DB parameter group To create an RDS for MySQL database, complete the following steps: On the Amazon RDS console, create a DB parameter group called zero-etl-custom-pg. Zero-ETL integration works by using binary logs (binlogs) generated by MySQL database. To enable binlogs on Amazon RDS for MySQL, a specific set of parameters must be enabled. Set the following binlog cluster parameter settings: binlog_format = ROW binlog_row_image = FULL binlog_checksum = NONE In addition, make sure that the binlog_row_value_options parameter is not set to PARTIAL_JSON. By default, this parameter is not set. Choose Databases in the navigation pane, then choose Create database. For Engine Version, choose MySQL 8.0.28 (or higher). For Templates, select Production. For Availability and durability, select either Multi-AZ DB instance or Single DB instance (Multi-AZ DB clusters are not supported, as of this writing). For DB instance identifier, enter zero-etl-source-rms. Under Instance configuration, select Memory optimized classes and choose the instance db.r6g.large, which should be sufficient for TICKIT use case. Under Additional configuration, for DB cluster parameter group, choose the parameter group you created earlier (zero-etl-custom-pg). Choose Create database. In a couple of minutes, it should spin up an RDS for MySQL database as the source for zero-ETL integration. Configure the Redshift destination After you create your source DB cluster, you must create and configure a target data warehouse in Amazon Redshift. The data warehouse must meet the following requirements: Using an RA3 node type (ra3.16xlarge, ra3.4xlarge, or ra3.xlplus) or Amazon Redshift Serverless Encrypted (if using a provisioned cluster) For our use case, create a Redshift cluster by completing the following steps: On the Amazon Redshift console, choose Configurations and then choose Workload management. In the parameter group section, choose Create. Create a new parameter group named zero-etl-rms. Choose Edit parameters and change the value of enable_case_sensitive_identifier to True. Choose Save. You can also use the AWS Command Line Interface (AWS CLI) command update-workgroup for Redshift Serverless: aws redshift-serverless update-workgroup --workgroup-name <your-workgroup-name> --config-parameters parameterKey=enable_case_sensitive_identifier,parameterValue=true Choose Provisioned clusters dashboard. At the top of you console window, you will see a Try new Amazon Redshift features in preview banner. Choose Create preview cluster. For Preview track, chose preview_2023. For Node type, choose one of the supported node types (for this post, we use ra3.xlplus). Under Additional configurations, expand Database configurations. For Parameter groups, choose zero-etl-rms. For Encryption, select Use AWS Key Management Service. Choose Create cluster. The cluster should become Available in a few minutes. Navigate to the namespace zero-etl-target-rs-ns and choose the Resource policy tab. Choose Add authorized principals. Enter either the Amazon Resource Name (ARN) of the AWS user or role, or the AWS account ID (IAM principals) that are allowed to create integrations. An account ID is stored as an ARN with root user. In the Authorized integration sources section, choose Add authorized integration source to add the ARN of the RDS for MySQL DB instance that’s the data source for the zero-ETL integration. You can find this value by going to the Amazon RDS console and navigating to the Configuration tab of the zero-etl-source-rms DB instance. Your resource policy should resemble the following screenshot. Configure required permissions To create a zero-ETL integration, your user or role must have an attached identity-based policy with the appropriate AWS Identity and Access Management (IAM) permissions. An AWS account owner can configure required permissions for users or roles who may create zero-ETL integrations. The sample policy allows the associated principal to perform the following actions: Create zero-ETL integrations for the source RDS for MySQL DB instance. View and delete all zero-ETL integrations. Create inbound integrations into the target data warehouse. This permission is not required if the same account owns the Redshift data warehouse and this account is an authorized principal for that data warehouse. Also note that Amazon Redshift has a different ARN format for provisioned and serverless clusters: Provisioned – arn:aws:redshift:{region}:{account-id}:namespace:namespace-uuid Serverless – arn:aws:redshift-serverless:{region}:{account-id}:namespace/namespace-uuid Complete the following steps to configure the permissions: On the IAM console, choose Policies in the navigation pane. Choose Create policy. Create a new policy called rds-integrations using the following JSON (replace region and account-id with your actual values): { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": [ "rds:CreateIntegration" ], "Resource": [ "arn:aws:rds:{region}:{account-id}:db:source-instancename", "arn:aws:rds:{region}:{account-id}:integration:*" ] }, { "Effect": "Allow", "Action": [ "rds:DescribeIntegration" ], "Resource": ["*"] }, { "Effect": "Allow", "Action": [ "rds:DeleteIntegration" ], "Resource": [ "arn:aws:rds:{region}:{account-id}:integration:*" ] }, { "Effect": "Allow", "Action": [ "redshift:CreateInboundIntegration" ], "Resource": [ "arn:aws:redshift:{region}:{account-id}:cluster:namespace-uuid" ] }] } Attach the policy you created to your IAM user or role permissions. Create the zero-ETL integration To create the zero-ETL integration, complete the following steps: On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane. Choose Create zero-ETL integration. For Integration identifier, enter a name, for example zero-etl-demo. For Source database, choose Browse RDS databases and choose the source cluster zero-etl-source-rms. Choose Next. Under Target, for Amazon Redshift data warehouse, choose Browse Redshift data warehouses and choose the Redshift data warehouse (zero-etl-target-rs). Choose Next. Add tags and encryption, if applicable. Choose Next. Verify the integration name, source, target, and other settings. Choose Create zero-ETL integration. You can choose the integration to view the details and monitor its progress. It took about 30 minutes for the status to change from Creating to Active. The time will vary depending on the size of your dataset in the source. Create a database from the integration in Amazon Redshift To create your database from the zero-ETL integration, complete the following steps: On the Amazon Redshift console, choose Clusters in the navigation pane. Open the zero-etl-target-rs cluster. Choose Query data to open the query editor v2. Connect to the Redshift data warehouse by choosing Save. Obtain the integration_id from the svv_integration system table: select integration_id from svv_integration; -- copy this result, use in the next sql Use the integration_id from the previous step to create a new database from the integration: CREATE DATABASE zetl_source FROM INTEGRATION '<result from above>'; The integration is now complete, and an entire snapshot of the source will reflect as is in the destination. Ongoing changes will be synced in near real time. Analyze the near real time transactional data Now we can run analytics on TICKIT’s operational data. Populate the source TICKIT data To populate the source data, complete the following steps: Copy the CSV input data files into a local directory. The following is an example command: aws s3 cp 's3://redshift-blogs/zero-etl-integration/data/tickit' . --recursive Connect to your RDS for MySQL cluster and create a database or schema for the TICKIT data model, verify that the tables in that schema have a primary key, and initiate the load process: mysql -h <rds_db_instance_endpoint> -u admin -p password --local-infile=1 Use the following CREATE TABLE commands. Load the data from local files using the LOAD DATA command. The following is an example. Note that the input CSV file is broken into several files. This command must be run for every file if you would like to load all data. For demo purposes, a partial data load should work as well. Analyze the source TICKIT data in the destination On the Amazon Redshift console, open the query editor v2 using the database you created as part of the integration setup. Use the following code to validate the seed or CDC activity: SELECT * FROM SYS_INTEGRATION_ACTIVITY ORDER BY last_commit_timestamp DESC; You can now apply your business logic for transformations directly on the data that has been replicated to the data warehouse. You can also use performance optimization techniques like creating a Redshift materialized view that joins the replicated tables and other local tables to improve query performance for your analytical queries. Monitoring You can query the following system views and tables in Amazon Redshift to get information about your zero-ETL integrations with Amazon Redshift: SVV_INTEGRATION – Provides configuration details for your integrations SYS_INTEGRATION_ACTIVITY– Provides information about completed integration runs SVV_INTEGRATION_TABLE_STATE – Describes the table-level integration information To view the integration-related metrics published to Amazon CloudWatch, open the Amazon Redshift console. Choose Zero-ETL integrations in the navigation pane and choose the integration to display activity metrics. Available metrics on the Amazon Redshift console are integration metrics and table statistics, with table statistics providing details of each table replicated from Amazon RDS for MySQL to Amazon Redshift. Integration metrics contain table replication success and failure counts and lag details. Manual resyncs The zero-ETL integration will automatically initiate a resync if a table sync state shows as failed or resync required. But in case the auto resync fails, you can initiate a resync at table-level granularity: ALTER DATABASE zetl_source INTEGRATION REFRESH TABLES tbl1, tbl2; A table can enter a failed state for multiple reasons: The primary key was removed from the table. In such cases, you need to re-add the primary key and perform the previously mentioned ALTER command. An invalid value is encountered during replication or a new column is added to the table with an unsupported data type. In such cases, you need to remove the column with the unsupported data type and perform the previously mentioned ALTER command. An internal error, in rare cases, can cause table failure. The ALTER command should fix it. Clean up When you delete a zero-ETL integration, your transactional data isn’t deleted from the source RDS or the target Redshift databases, but Amazon RDS doesn’t send any new changes to Amazon Redshift. To delete a zero-ETL integration, complete the following steps: On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane. Select the zero-ETL integration that you want to delete and choose Delete. To confirm the deletion, choose Delete. Conclusion In this post, we showed you how to set up a zero-ETL integration from Amazon RDS for MySQL to Amazon Redshift. This minimizes the need to maintain complex data pipelines and enables near real time analytics on transactional and operational data. To learn more about Amazon RDS zero-ETL integration with Amazon Redshift, refer to Working with Amazon RDS zero-ETL integrations with Amazon Redshift (preview). About the Authors Milind Oke is a senior Redshift specialist solutions architect who has worked at Amazon Web Services for three years. He is an AWS-certified SA Associate, Security Specialty and Analytics Specialty certification holder, based out of Queens, New York. Aditya Samant is a relational database industry veteran with over 2 decades of experience working with commercial and open-source databases. He currently works at Amazon Web Services as a Principal Database Specialist Solutions Architect. In his role, he spends time working with customers designing scalable, secure and robust cloud native architectures. Aditya works closely with the service teams and collaborates on designing and delivery of the new features for Amazon’s managed databases. View the full article

Amazon Aurora MySQL zero-ETL integration with Amazon Redshift now supports data filtering, enabling you to include or exclude specific databases and tables as part of the zero-ETL integration. Based on your analytics needs, filtering of specific databases and tables helps you selectively bring data into Amazon Redshift. In addition, you can now easily manage and automate the configuration and deployment of resources needed for an Aurora MySQL zero-ETL integration with Amazon Redshift using AWS CloudFormation. View the full article

As your organization becomes more data driven and uses data as a source of competitive advantage, you’ll want to run analytics on your data to better understand your core business drivers to grow sales, reduce costs, and optimize your business. To run analytics on your operational data, you might build a solution that is a combination of a database, a data warehouse, and an extract, transform, and load (ETL) pipeline. ETL is the process data engineers use to combine data from different sources. To reduce the effort involved in building and maintaining ETL pipelines between transactional databases and data warehouses, AWS announced Amazon Aurora zero-ETL integration with Amazon Redshift at AWS re:Invent 2022 and is now generally available (GA) for Amazon Aurora MySQL-Compatible Edition 3.05.0. AWS is now announcing data filtering on zero-ETL integrations, enabling you to bring in selective data from the database instance on zero-ETL integrations between Amazon Aurora MySQL and Amazon Redshift. This feature allows you to select individual databases and tables to be replicated to your Redshift data warehouse for analytics use cases. In this post, we provide an overview of use cases where you can use this feature, and provide step-by-step guidance on how to get started with near real time operational analytics using this feature. Data filtering use cases Data filtering allows you to choose the databases and tables to be replicated from Amazon Aurora MySQL to Amazon Redshift. You can apply multiple filters to the zero-ETL integration, allowing you to tailor the replication to your specific needs. Data filtering applies either an exclude or include filter rule, and can use regular expressions to match multiple databases and tables. In this section, we discuss some common use cases for data filtering. Improve data security by excluding tables containing PII data from replication Operational databases often contain personally identifiable information (PII). This is information that is sensitive in nature, and can include information such as mailing addresses, customer verification documentation, or credit card information. Due to strict security compliance regulations, you may not want to use PII for your analytics use cases. Data filtering allows you to filter out databases or tables containing PII data, excluding them from replication to Amazon Redshift. This improves data security and compliance with analytics workloads. Save on storage costs and manage analytics workloads by replicating tables required for specific use cases Operational databases often contain many different datasets that aren’t useful for analytics. This includes supplementary data, specific application data, and multiple copies of the same dataset for different applications. Moreover, it’s common to build different use cases on different Redshift warehouses. This architecture requires different datasets to be available in individual endpoints. Data filtering allows you to only replicate the datasets that are required for your use cases. This can save costs by eliminating the need to store data that is not being used. You can also modify existing zero-ETL integrations to apply more restrictive data replication where desired. If you add a data filter to an existing integration, Aurora will fully reevaluate the data being replicated with the new filter. This will remove the newly filtered data from the target Redshift endpoint. For more information about quotas for Aurora zero-ETL integrations with Amazon Redshift, refer to Quotas. Start with small data replication and incrementally add tables as required As more analytics use cases are developed on Amazon Redshift, you may want to add more tables to an individual zero-ETL replication. Rather than replicating all tables to Amazon Redshift to satisfy the chance that they may be used in the future, data filtering allows you to start small with a subset of tables from your Aurora database and incrementally add more tables to the filter as they’re required. After a data filter on a zero-ETL integration is updated, Aurora will fully reevaluate the entire filter as if the previous filter didn’t exist, so workloads using previously replicated tables aren’t impacted in the addition of new tables. Improve individual workload performance by load balancing replication processes For large transactional databases, you may need to load balance the replication and any downstream processing to multiple Redshift clusters to allow for reduction of compute requirements for an individual Redshift endpoint and the ability to split workloads onto multiple endpoints. By load balancing workloads across multiple Redshift endpoints, you can effectively create a data mesh architecture, where endpoints are appropriately sized for individual workloads. This can improve performance and lower overall cost. Data filtering allows you to replicate different databases and tables to separate Redshift endpoints. The following figure shows how you could use data filters on zero-ETL integrations to split different databases in Aurora to separate Redshift endpoints. Example use case Consider the TICKIT database. The TICKIT sample database contains data from a fictional company where users can buy and sell tickets for various events. The company’s business analysts want to use the data that is stored in their Aurora MySQL database to generate various metrics, and would like to perform this analysis in near real time. For this reason, the company has identified zero-ETL as a potential solution. Throughout their investigation of the datasets required, the company’s analysts noted that the users table contains personal information about their customer user information that is not useful for their analytics requirements. Therefore, they want to replicate all data except the users table and will use zero-ETL’s data filtering to do so. Setup Start by following the steps in Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift to create a new Aurora MySQL database, Amazon Redshift Serverless endpoint, and zero-ETL integration. Then open the Redshift query editor v2 and run the following query to show that data from the users table has been replicated successfully: select * from aurora_zeroetl.demodb.users; Data filters Data filters are applied directly to the zero-ETL integration on Amazon Relational Database Service (Amazon RDS). You can define multiple filters for a single integration, and each filter is defined as either an Include or Exclude filter type. Data filters apply a pattern to existing and future database tables to determine which filter should be applied. Apply a data filter To apply a filter to remove the users table from the zero-ETL integration, complete the following steps: On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane. Choose the zero-ETL integration to add a filter to. The default filter is to include all databases and tables represented by an include:*.* filter. Choose Modify. Choose Add filter in the Source section. For Choose filter type, choose Exclude. For Filter expression, enter the expression demodb.users. Filter expression order matters. Filters are evaluated left to right, top to bottom, and subsequent filters will override previous filters. In this example, Aurora will evaluate that every table should be included (filter 1) and then evaluate that the demodb.users table should be excluded (filter 2). The exclusion filter therefore overrides the inclusion because it’s after the inclusion filter. Choose Continue. Review the changes, making sure that the order of the filters is correct, and choose Save changes. The integration will be added and will be in a Modifying state until the changes have been applied. This can take up to 30 minutes. To check if the changes have finished applying, choose the zero-ETL integration and check its status. When it shows as Active, the changes have been applied. Verify the change To verify the zero-ETL integration has been updated, complete the following steps: In the Redshift query editor v2, connect to your Redshift cluster. Choose (right-click) the aurora-zeroetl database you created and choose Refresh. Expand demodb and Tables. The users table is no longer available because it has been removed from the replication. All other tables are still available. If you run the same SELECT statement from earlier, you will receive an error stating the object does not exist in the database: select * from aurora_zeroetl.demodb.users; Apply a data filter using the AWS CLI The company’s business analysts now understand that more databases are being added to the Aurora MySQL database and they want to ensure only the demodb database is replicated to their Redshift cluster. To this end, they want to update the filters on the zero-ETL integration with the AWS Command Line Interface (AWS CLI). To add data filters to a zero-ETL integration using the AWS CLI, you can call the modify-integration command. In addition to the integration identifier, specify the --data-filter parameter with a comma-separated list of include and exclude filters. Complete the following steps to alter the filter on the zero-ETL integration: Open a terminal with the AWS CLI installed. Enter the following command to list all available integrations: aws rds describe-integrations Find the integration you want to update and copy the integration identifier. The integration identifier is an alphanumeric string at the end of the integration ARN. Run the following command, updating <integration identifier> with the identifier copied from the previous step: aws rds modify-integration --integration-identifier "<integration identifier>" --data-filter 'exclude: *.*, include: demodb.*, exclude: demodb.users' When Aurora is assessing this filter, it will exclude everything by default, then only include the demodb database, but exclude the demodb.users table. Data filters can implement regular expressions for the databases and table. For example, if you want to filter out any tables starting with user, you can run the following: aws rds modify-integration --integration-identifier "<integration identifier>" --data-filter 'exclude: *.*, include: demodb.*, exclude *./^user/' As with the previous filter change, the integration will be added and will be in a Modifying state until the changes have been applied. This can take up to 30 minutes. When it shows as Active, the changes have been applied. Clean up To remove the filter added to the zero-ETL integration, complete the following steps: On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane. Choose your zero-ETL integration. Choose Modify. Choose Remove next to the filters you want to remove. You can also change the Exclude filter type to Include. Alternatively, you can use the AWS CLI to run the following: aws rds modify-integration --integration-identifier "<integration identifier>" --data-filter 'include: *.*' Choose Continue. Choose Save changes. The data filter will take up to 30 minutes to apply the changes. After you remove data filters, Aurora reevaluates the remaining filters as if the removed filter had never existed. Any data that previously didn’t match the filtering criteria but now does is replicated into the target Redshift data warehouse. Conclusion In this post, we showed you how to set up data filtering on your Aurora zero-ETL integration from Amazon Aurora MySQL to Amazon Redshift. This allows you to enable near real time analytics on transactional and operational data while replicating only the data required. With data filtering, you can split workloads into separate Redshift endpoints, limit the replication of private or confidential datasets, and increase performance of workloads by only replicating required datasets. To learn more about Aurora zero-ETL integration with Amazon Redshift, see Working with Aurora zero-ETL integrations with Amazon Redshift and Working with zero-ETL integrations. About the authors Jyoti Aggarwal is a Product Management Lead for AWS zero-ETL. She leads the product and business strategy, including driving initiatives around performance, customer experience, and security. She brings along an expertise in cloud compute, data pipelines, analytics, artificial intelligence (AI), and data services including databases, data warehouses and data lakes. Sean Beath is an Analytics Solutions Architect at Amazon Web Services. He has experience in the full delivery lifecycle of data platform modernisation using AWS services, and works with customers to help drive analytics value on AWS. Gokul Soundararajan is a principal engineer at AWS and received a PhD from University of Toronto and has been working in the areas of storage, databases, and analytics. View the full article

The HashiCorp Terraform ecosystem continues to expand with new integrations that provide additional capabilities to Terraform Cloud, Enterprise, and Community edition users as they provision and manage their cloud and on-premises infrastructure. Terraform is the world’s most widely used multi-cloud provisioning product. Whether you're deploying to Amazon Web Services (AWS), Microsoft Azure, Google Cloud, other cloud and SaaS offerings, or an on-premises datacenter, Terraform can be your single control plane, using infrastructure as code for infrastructure automation to provision and manage your entire infrastructure. Terraform Cloud run tasks Run tasks allow platform teams to easily extend the Terraform Cloud run lifecycle with additional capabilities offered by services from partners. Wiz Wiz, makers of agentless cloud security and compliance for AWS, Azure, Google Cloud, and Kubernetes, launched a new integration with Terraform run tasks that ensures only secure infrastructure is deployed. Acting as a guardrail, it prevents insecure deployments by scanning using predefined security policies, helping to reduce the organization's overall risk exposure. Terraform providers We’ve also approved 17 new verified Terraform providers from 13 different partners: AccuKnox AccuKnox, maker of a zero trust CNAPP (Cloud Native Application Protection) platform, has released the AccuKnox provider for Terraform, which allows for managing KubeArmor resources on Kubernetes clusters or host environments. Chainguard Chainguard, which offers Chainguard Images, a collection of secure minimal container images, released two Terraform providers: the Chainguard Terraform provider to manage Chainguard resources (IAM groups, identities, image repos, etc.) via Terraform, and the imagetest provider for authoring and executing tests using Terraform primitives, designed to work in conjunction with the Chainguard Images project. Cisco Systems Cisco delivers software-defined networking, cloud, and security solutions to help transform your business. Cisco DevNet has released two new providers for the Cisco Multicloud Defense and Cisco Secure Workload products: The Multicloud Defense provider is used to create and manage Multicloud Defense resources such as service VPCs/VNets, gateways, policy rulesets, address objects, service objects, and others. The Cisco Secure Workload provider can be used to manage the secure workload configuration when setting up workload protection policies for various environments. Citrix Citrix, maker of secure, unified digital workspace technology, developed a custom Terraform provider for automating Citrix product deployments and configurations. Using the Terraform with Citrix provider, users can manage Citrix products via infrastructure as code, giving greater efficiency and consistency on infrastructure management, as well as better reusability on infrastructure configuration. Couchbase Couchbase, which manages a distributed NoSQL cloud database, has released the Terraform Couchbase Capella provider to deploy, update, and manage Couchbase Capella infrastructure as code. Genesis Cloud Genesis Cloud offers accelerated cloud GPU computing for machine learning, visual effects rendering, big data analytics, and cognitive computing. The Genesis Cloud Terraform provider is used to interact with resources supported by Genesis Cloud via public API. Hund Hund offers automated monitoring to provide companies with simplified product transparency, from routine maintenance to critical system failures. The company recently published a new Terraform provider that offers resources/data sources to allow practitioners to manage objects on Hund’s hosted status page platform. Managed objects can include components, groups, issues, templates, and more. Mondoo Mondoo creates an index of all cloud, Kubernetes, and on-premises resources to help identify misconfigurations, ensure security, and support auditing and compliance. The company has released a new Mondoo Terraform provider to allow Terraform to manage Mondoo resources. Palo Alto Networks Palo Alto Networks is a multi-cloud security company. It has released a new Terraform provider for Strata Cloud Manager (SCM) that focuses on configuring the unified networking security aspect of SCM. Ping Identity Ping Identity delivers identity solutions that enable companies to balance security and personalized, streamlined user experiences. Ping has released two Terraform providers: The PingDirectory Terraform provider is a plugin for Terraform that supports the management of PingDirectory configuration, while the PingFederate Terraform provider is a plugin for Terraform that supports the management of PingFederate configuration. SquaredUp SquaredUp manages a visualization platform to help enterprises build, run, and optimize complex digital services by surfacing data faster. The company has released a new SquaredUp Terraform provider to help bring a unified visibility across teams and tools for greater insights and observability in your platform. Traceable Traceable is an API security platform that identifies and tests APIs, evaluates API risk posture, stops API attacks, and provides deep analytics for threat hunting and forensic research. The company recently released two integrations: a custom Terraform provider for AWS API Gateways and a Terraform Lambda-based resource provider. These providers allow the deployment of API security tooling to reduce the risk of API security events. VMware VMware offers a breadth of digital solutions that power apps, services, and experiences for their customers. The NSX-T VPC Terraform provider gives NSX VPC administrators a way to automate NSX's virtual private cloud to provide virtualized networking and security services. Learn more about Terraform integrations All integrations are available for review in the HashiCorp Terraform Registry. To verify an existing integration, please refer to our Terraform Cloud Integration Program. If you haven’t already, try the free tier of Terraform Cloud to help simplify your Terraform workflows and management. View the full article

Businesses face continuous pressure to deliver high-quality IT services efficiently. One emerging integration to meet these goals is between DevOps and IT service management (ITSM), more commonly known as DevOps+. DevOps+ is becoming more than a trend and has emerged as a powerful collaborative approach to meet these challenges. By combining the strengths of DevOps’ […] View the full article

AWS announces the support for Amazon Redshift with Visual Studio Code (VSCode), a free and open-source code editor. The integration with Visual Studio Code enables Amazon Redshift customers to use Visual Studio Code to author and run their SQL queries in a notebook interface and view the schema objects in their Redshift data warehouses. View the full article

The post How to Integrate Redmine with ONLYOFFICE Docs on Linux first appeared on Tecmint: Linux Howtos, Tutorials & Guides .For Linux users, the choice of ideal software for project management might be a serious challenge due to the variety of available tools suited to The post How to Integrate Redmine with ONLYOFFICE Docs on Linux first appeared on Tecmint: Linux Howtos, Tutorials & Guides.View the full article

Cloud-native integration platforms have emerged as potent drivers of business transformation, enabling seamless connections between diverse applications and systems. This grants enterprises remarkable agility, scalability, and operational efficiency. This informative blog delves into the world of leading cloud-native integration platforms, spearheading significant changes in the business arena. By enhancing customer experiences and streamlining internal processes, these platforms have the capacity to revolutionize modern business operations at their essence. The adoption of cloud-native integration platforms represents a strategic move to meet the evolving demands of the digital era. These platforms not only facilitate smooth connections but also enable organizations to adeptly navigate complexity, respond to change promptly, and effortlessly enhance their capabilities. They effectively integrate diverse systems and applications. View the full article

Jaeger has gained significant popularity in the software development community due to its powerful capabilities and ease of integration with various programming languages and frameworks. With the rise of microservices and cloud-native applications, Jaeger has become a crucial tool for developers and system administrators to gain insights into the performance and behavior of their applications. How do you make Jaeger even more effective for monitoring and troubleshooting distributed applications, especially in high-traffic, demanding environments where a high-performance storage solution is critical? Use the best-performing Jaeger storage backend that you can find. View the full article

How to use the MongoDB connector with Application IntegrationView the full article

The sheer number of tools, libraries, and frameworks can give many programmers a headache. Moreover, complex designs often require many of these components to work together or at least not interfere with each other. Database versioning — and integration tests during which we conduct it — are great examples of such problematic cooperation. There is also the aspect of the persistence layer in our code, which will be the subject of the above-mentioned tests. View the full article

AWS Glue now supports GitLab and BitBucket, alongside GitHub and AWS CodeCommit, broadening your toolset for managing data integration pipeline deployments. AWS Glue is a serverless data integration service that makes it simpler to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development. View the full article

Atlassian today made it simpler to navigate the Jira Software and Jira Work Management project management applications. View the full article

Tray.io's Universal Automation Cloud service leverages an existing Merlin AI engine to identify the most efficient way to automate a workflow. View the full article

Data is the lifeblood of any business, and AI is the key to unlocking its full potential. However, harnessing the volume and complexity of data from apps, services, sensors, and more can be a significant challenge to fueling impactful AI. By investing in a unified, intelligent data platform, organizations can not only save on integration costs and improve their security posture, but also enable more advanced AI capabilities. Healthcare companies like Mercy, manufacturers like Aurobay, Financial Services providers like Manulife, and other companies across industries are paving the way for AI innovation by modernizing their data with Microsoft Azure. The rise of generative AI presents an especially exciting opportunity to transform your data into a competitive advantage. For example, Atera is transforming IT Management by combining foundation models in Azure OpenAI Service with unique business data, providing customers with an all-encompassing view of IT activities to proactively identify issues, and recommend immediate solutions. Likewise, startup Commerce.AI is driving 30 to 50 percent increases in productivity for customers by using Azure OpenAI Service to extract insights from unstructured data, like customer support calls and online reviews, uncovering new growth opportunities, and automating workflows. By using foundation models as a reasoning engine—not a knowledge base—organizations can build more relevant, differentiated solutions for customers and end-users. In this month’s blog post, I’ll explore new ways that organizations can connect their business data to Azure AI to unearth meaningful insights, predictions, and actions. First, here are new learning and skilling resources to help your teams get inspired and build a proof of concept quickly... View the full article

Amazon Personalize launches a new integration with self-managed OpenSearch that enables customers to personalize search results for each user and assists in predicting their search needs. The Amazon Personalize Search Ranking plugin within OpenSearch helps customers to leverage the deep learning capabilities offered by Amazon Personalize and add personalization to OpenSearch search results, without any ML expertise. View the full article