Best Data Management Software for Kinetica

Oracle Big Data Preparation Cloud Service (PaaS), is a cloud-based managed Platform as a Service (PaaS). It allows you to quickly ingest, repair and enrich large data sets in an interactive environment. For down-stream analysis, you can integrate your data to other Oracle Cloud Services such as Oracle Business Intelligence Cloud Service. Oracle Big Data Preparation Cloud Service has important features such as visualizations and profile metrics. Visual access to profile results and summary for each column are available when a data set has been ingested. You also have visual access the duplicate entity analysis results on the entire data set. You can visualize governance tasks on the service homepage with easily understandable runtime metrics, data quality reports and alerts. Track your transforms to ensure that files are being processed correctly. The entire data pipeline is visible, from ingestion through enrichment and publishing.

Third-party data enriched with third-party data can be used to improve campaign performance and easily create audience segments. Oracle Audience Segmentation uses customer analytics to predict campaign performance and measure it. You can sort through your customer data without SQL. Drag-and-drop tools allow you to create segments and include important information to personalize every message. Segment your marketing efforts based on aggregated customer value. You can filter easily to identify the top 15% of buyers and isolate the least engaged web visitors. You can save time and improve the accuracy of your targeting. You can quickly assess the size of an audience and not have to wait for a query response. Waterfall segmentation allows for you to create and prioritize segments within your audience to match customers with relevant, goal-oriented content. This segment data will be included in the audience when it is published to external systems such as Responsys Campaign Management.

Machine learning uncovers hidden patterns in enterprise data and generates new value for businesses. Oracle Machine Learning makes it easier to create and deploy machine learning models for data scientists by using AutoML technology and reducing data movement. It also simplifies deployment. Apache Zeppelin notebook technology, which is open-source-based, can increase developer productivity and decrease their learning curve. Notebooks are compatible with SQL, PL/SQL and Python. Users can also use markdown interpreters for Oracle Autonomous Database to create models in their preferred language. No-code user interface that supports AutoML on Autonomous Database. This will increase data scientist productivity as well as non-expert users' access to powerful in-database algorithms to classify and regression. Data scientists can deploy integrated models using the Oracle Machine Learning AutoML User Interface.

Oracle Cloud Infrastructure (OCI Data Catalog) is a metadata management tool that aids data professionals in discovering data and supporting data governance. It was designed to integrate with the Oracle ecosystem. It provides an inventory of assets and a business glossary. OCI Data Catalog is fully managed and maintained by Oracle. It runs on all the power and scale that Oracle Cloud Infrastructure offers. OCI Data Catalog offers all the security, reliability and performance of Oracle Cloud. Developers can integrate OCI Data Catalog's capabilities into their own applications by using REST APIs or SDKs. Administrators can manage access to OCI Data Catalog objects and security requirements by using a trusted system to manage user identities and access privileges. To get real value out of data, discover data assets in Oracle data stores both on-premises or in the cloud.

Gravity's mission, to make streaming data from over 100 sources easy and only pay for what you use, is Gravity. Gravity eliminates the need for engineering teams to deliver streaming pipelines. It provides a simple interface that allows streaming to be set up in minutes using event data, databases, and APIs. All members of the data team can now create with a simple point-and-click interface so you can concentrate on building apps, services, and customer experiences. For quick diagnosis and resolution, full Execution trace and detailed error messages are available. We have created new, feature-rich methods to help you quickly get started. You can set up bulk, default schemas, and select data to access different job modes and statuses. Our intelligent engine will keep your pipelines running, so you spend less time managing infrastructure and more time analysing it. Gravity integrates into your systems for notifications, orchestration, and orchestration.

The RAPIDS software library, which is built on CUDAX AI, allows you to run end-to-end data science pipelines and analytics entirely on GPUs. It uses NVIDIA®, CUDA®, primitives for low level compute optimization. However, it exposes GPU parallelism through Python interfaces and high-bandwidth memories speed through user-friendly Python interfaces. RAPIDS also focuses its attention on data preparation tasks that are common for data science and analytics. This includes a familiar DataFrame API, which integrates with a variety machine learning algorithms for pipeline accelerations without having to pay serialization fees. RAPIDS supports multi-node, multiple-GPU deployments. This allows for greatly accelerated processing and training with larger datasets. You can accelerate your Python data science toolchain by making minimal code changes and learning no new tools. Machine learning models can be improved by being more accurate and deploying them faster.