Ventera’s Post

Trusting your AI models begins with trusting the data that feeds into them. I’m so excited to share that the latest release of DataStage on Cloud Pak for Data 5.0 is now available! Our new features make it easier than ever for users to transform data anywhere, anytime, and ensure high data quality to power AI workloads confidently. Check out my blog post to discover everything new with DataStage: https://ibm.biz/Bdm7cf Learn about the latest updates in CP4D 5.0 here: https://ibm.biz/Bdm7cy #CP4D #CloudPakforData #IBMDatastage #DataStage

Crafting an Event-Driven ETL Workflow with AWS: A Comprehensive Guide #AWS #businesscompassllc #cloud

The article discusses the challenges businesses face in preparing data for AI applications and how IBM databases on Amazon Web Services (AWS) offer solutions. It highlights issues such as data silos, duplication, and data quality concerns, as well as the time-consuming nature of traditional database management tasks. The proposed solution is IBM's portfolio of database solutions on AWS, which allows enterprises to scale applications, analytics, and AI across hybrid cloud environments. It emphasizes the need for a flexible, cloud-native infrastructure to address these challenges and scale according to AI workload demands. The article details the features and benefits of various IBM database solutions on AWS, such as Amazon RDS for Db2, Db2 Warehouse SaaS, Netezza SaaS, and watsonx.data SaaS. Success stories of businesses reducing costs, improving scalability, and accelerating decision-making by adopting IBM database solutions on AWS are highlighted. The article concludes by encouraging businesses to begin their data modernization journey using IBM database solutions available on the AWS Marketplace. https://lnkd.in/dBMQktZw

#DP-203 #Azure #cloudcomputing #Microsoft ⛅ Continuous learning (ADF)⛅ ☀️A very important concept to understand: Azure Integration Runtime (IR) A Fully managed service by Microsoft Azure, facilitating seamless data movement and integration within Azure services. Optimized for transferring data between various Azure data stores and compute services. ⚡ Why do I need an Integration Runtime? When deploying services in the cloud that require data from an existing on-premise network, you need to either move that data to the cloud or make it available to the new cloud service from its current location. ⚡ How is it used? ADF is the most commonly used data movement service, comprised of pipelines that run activities against your data. Types of IR: ✅ Azure Integration Runtime: This runtime is used to connect to Azure data stores such as Azure Blob Storage, ADLS, Azure SQL Database, and Azure Synapse Analytics. ✅ Self-Hosted Integration Runtime: This runtime is installed on an on-premises machine or a virtual machine (VM) in a private network to provide secure connectivity between the on-premises data stores and the Azure Data Factory service. ✅ Azure-SSIS Integration Runtime: This runtime is used to execute SQL Server Integration Services (SSIS) packages in the cloud. ✅ Azure Function App Integration Runtime: This runtime allows you to execute Azure Functions as part of an ADF pipeline. It provides server-less compute to run code in response to events or specific triggers. ⚡ Interview Question: How to migrate your on-prem data to cloud using ADF 📍Answer: With the help of Self-Hosted Integration Runtime (IR) 📍How: The Self-Hosted IR is installed on a machine in your on-premises environment and acts as a secure communication channel between the ADF and the on-premises or other cloud data stores. Use Cases of Self-Hosted (IR): 🚀It provides a gateway for ADF to access the data stores that are not accessible over the public internet, allowing you to create hybrid data integration scenarios. 🚀Allows you to create hybrid data integration scenarios, such as data synchronization, data migration, and data transformation. 🚀Provides secure connectivity by establishing a private connection between the ADF service and the on-premises. 🚀Enables you to run custom activities and code on on-premises to perform specific data integration tasks that ADF does not natively support. 🚀Allows you to connect to data stores that require specialized connectors or drivers that are not available in ADF natively. 🚀Allows you to create hybrid data integration scenarios that leverage both cloud and on-premises resources. ☀️☀️☀️Happy Learning☀️☀️☀️ #dataengineering #dataanalytics #businessintelligence #BigData #knowledgecheck

As many head to the Databricks Data+AI Summit next week, I wanted to resurface this blog. Read-on to learn how the Informatica Cloud Modernization Service is helping PowerCenter customers more easily modernize their existing data warehouses to Databricks and their existing classical data flows to use modern integration patterns. Our customers have reduced their time-to-value by eight times and lowered the cost of migration by 50 percent. Good stuff! #Informatica #Databricks

Our latest article provides a detailed guide on starting and stopping Azure Data Factory (ADF) Integration Runtime using PowerShell and Azure Automation Account. This approach can help optimize resource management and control costs by automating ADF operations. https://lnkd.in/dWr7zZth #Azure #DataFactory #PowerShell #Automation #AzureAutomation

[Article] What I Wish Everyone Knew About ETL Processes - Traditional to Modern Cloud Solutions >>> https://lnkd.in/eHVArRqx Learn about the evolutionary journey of ETL (Extract, Transform, Load) from traditional processes to modern cloud solutions. Author: Amira Bedhiafi #sql #sqlserver #microsoftsqlserver #mssql #mssqlserver #sqlbi #businessintelligence #etl #importandexport #cloud #datawarehousing

Discover why pagination is a game-changer for large datasets sourced from REST APIs. Explore custom solutions and best practices to optimize your #ETL pipelines. Read the full article. #AzureCloud #ETLAutomation #DataEngineering #Azure #AzureDataFactory #dataextraction

Connect to Salesforce Data Cloud Ingestion API using C# and HttpClient