Best Data Integration Tools for Oracle Cloud Infrastructure

IBM Cloud Pak For Integration®, a hybrid integration platform, is an automated, closed-loop system that supports multiple styles and types of integration in a single, unified experience. Connect cloud and on-premise apps to unlock business data and assets, securely move data with enterprise messaging, deliver event interactions, transfer data across all clouds, and deploy and scale with shared foundational services and cloud-native architecture. All this is done with enterprise-grade encryption and security. Automated, closed-loop, and multi-style integrations deliver the best results. Targeted innovations can be used to automate integrations. These include natural language-powered flows, AI-assisted maps and RPA. You can also use company-specific operational information to continuously improve integrations and API test generation. Workload balancing can also be achieved.

Ascend provides data teams with a unified platform that allows them to ingest and transform their data and create and manage their analytics engineering and data engineering workloads. Ascend is supported by DataAware intelligence. Ascend works in the background to ensure data integrity and optimize data workloads, which can reduce maintenance time by up to 90%. Ascend's multilingual flex-code interface allows you to use SQL, Java, Scala, and Python interchangeably. Quickly view data lineage and data profiles, job logs, system health, system health, and other important workload metrics at a glance. Ascend provides native connections to a growing number of data sources using our Flex-Code data connectors.

Integrate your data sources including relational and NoSQL database, SaaS and APIs. You can query them immediately as a single source of data. Process data wherever you are. Data from different sources can be merged, retrieved, and cached. Use webhooks for streaming data from Kafka or Segment into the Peaka Table. Real-time data access replaces nightly batch ingestion. Treat each data source as a relational database. Convert any API into a table and combine and join it with other data sources. Use familiar SQL to run queries on NoSQL databases. The same skills can be used to retrieve data from SQL and NoSQL database. You can query and filter your consolidated datasets to create new data sets. Use APIs to expose them and serve other apps or systems. Don't get bogged down with scripts and logs when setting up your data stack. Eliminate the burdens of managing and maintaining ETL pipelines.

Connect and centralize data. Transform your data landscape into a flexible powerhouse. Data Virtuality is a data integration platform that allows for instant data access, data centralization, and data governance. Logical Data Warehouse combines materialization and virtualization to provide the best performance. For high data quality, governance, and speed-to-market, create your single source data truth by adding a virtual layer to your existing data environment. Hosted on-premises or in the cloud. Data Virtuality offers three modules: Pipes Professional, Pipes Professional, or Logical Data Warehouse. You can cut down on development time up to 80% Access any data in seconds and automate data workflows with SQL. Rapid BI Prototyping allows for a significantly faster time to market. Data quality is essential for consistent, accurate, and complete data. Metadata repositories can be used to improve master data management.

With exceptional functionality for data integration, quality and cleansing, maximize the value of all structured and unstructured data in your organization. SAP Data Services software increases the quality of enterprise data. It is part of SAP's Information Management Layer. It delivers timely, relevant, and trusted information to help drive better business outcomes. Transform your data into a reliable, always-available resource for business insights and use it to streamline operations and maximize efficiency. Get contextual insight and unlock the true potential of your data with a complete view of all your information. Access to any size data and any source. Standardizing and matching data can improve decision-making and operational efficiency. This will reduce duplicates, identify relationships and address quality issues proactively. Use intuitive tools to unify critical data whether it is on-premise, in the cloud or within Big Data.

Hadoop has been an integral part of the data management landscape since 2010. The majority of organizations that have adopted Hadoop over the past decade to build their data lake infrastructure. Although Hadoop was a cost-effective method to store petabytes in a distributed environment, there were many complexities. The systems required IT skills that were specialized and the on-premises environments did not allow for the ability to scale up or down as the usage requirements changed. Cloud computing addresses the complexity and flexibility issues associated with Hadoop environments on-premises. Many companies have chosen to automate cloud data migration using WANdisco to reduce the risks and cost associated with modernizing their data. LiveData Migrator can be used as a self-service tool and does not require any WANdisco services or expertise.

Users face integration challenges when using common dashboards, reporting and ETL tools. Simba provides ready-to-use drivers that are based on standards and ensure compatibility. This simplifies the connectivity process. Data providers struggle to provide their users with a seamless, easy data connection. Simba's SDK enables developers to create custom, standards based drivers, making connectivity easier than CSV exports or API-based access. Connectivity can be complicated by unique backend requirements such as implementation needs dictated from specific applications or internal processes. Simba's SDK and managed services allow the creation of drivers that meet these requirements. Simba offers comprehensive ODBC/JDBC extension for a variety of applications and data tools. Simba Drivers can be integrated into these tools in order to enhance their functionality and provide additional connectivity to data sources.