Business Software for AWS Glue - Page 2

At the intersection of data governance, privacy, and security, Privacera’s unified data access governance platform maximizes the value of data by providing secure data access control and governance across hybrid- and multi-cloud environments. The hybrid platform centralizes access and natively enforces policies across multiple cloud services—AWS, Azure, Google Cloud, Databricks, Snowflake, Starburst and more—to democratize trusted data enterprise-wide without compromising compliance with regulations such as GDPR, CCPA, LGPD, or HIPAA. Trusted by Fortune 500 customers across finance, insurance, retail, healthcare, media, public and the federal sector, Privacera is the industry’s leading data access governance platform that delivers unmatched scalability, elasticity, and performance. Headquartered in Fremont, California, Privacera was founded in 2016 to manage cloud data privacy and security by the creators of Apache Ranger™ and Apache Atlas™.

We’ve released a revolutionary big data platform that gathers all information about a music business, providing a single source of truth for decisions. Every music business has hundreds of data sources. But they are siloed and fragmented. Our platform identifies and connects them to build a foundation of quality data that can be applied to all daily music business operations. To work efficiently and securely—and to surface valuable insight no one else can—record labels and agencies require a sophisticated data management and governance system, so that data is available, relevant, and usable at all times. As data sources are ingested into Wavo’s Big Data Platform, machine learning is deployed to tag data based on personalized templates, making it easy to access and drill-down into important information. This enables everyone in a music business to activate and deliver business-ready data, backed up and organized for immediate value.

The Alex Platform is your enterprise’s single source of data and business truth. Alex is a foundational pillar of our customer’s data-driven success. From day one of implementation, Alex is designed to start reducing complexity and creating value immediately. Alex Augmented Data Catalog is powered by the industry’s best machine learning, rapidly providing a unified, enterprise-wide data platform. No matter how complex your technical landscape may be, Alex Data Lineage helps you map and understand your data flows in an automated and secure way. Worldwide teams need worldwide coordination. Alex Intelligent Business Glossary’s beautiful UI and rich functionality is perfect for conducting global collaboration. Combat complexity of the multi-cloud and global enterprise by unifying all definitions, policies, metrics, rules, processes, workflows and more. Power global data governance programs.

Discover & trust data for your analysis and models. Be more productive by breaking silos. Get immediate context into the data and see how others are using it. Search for data within your organization by a simple text search. A PageRank-inspired search algorithm recommends results based on names, descriptions, tags, and querying/viewing activity on the table/dashboard. Build trust in data using automated and curated metadata, descriptions of tables and columns, other frequent users, when the table was last updated, statistics, a preview of the data if permitted, etc. Easy triage by linking the ETL job and code that generated the data. Update tables and columns with descriptions, reduce unnecessary back and forth about which table to use and what a column contains. See what data fellow co-workers frequently use, own or have bookmarked. Learn what most common queries for a table look like by seeing dashboards built on a given table.

Varada’s dynamic and adaptive big data indexing solution enables to balance performance and cost with zero data-ops. Varada’s unique big data indexing technology serves as a smart acceleration layer on your data lake, which remains the single source of truth, and runs in the customer cloud environment (VPC). Varada enables data teams to democratize data by operationalizing the entire data lake while ensuring interactive performance, without the need to move data, model or manually optimize. Our secret sauce is our ability to automatically and dynamically index relevant data, at the structure and granularity of the source. Varada enables any query to meet continuously evolving performance and concurrency requirements for users and analytics API calls, while keeping costs predictable and under control. The platform seamlessly chooses which queries to accelerate and which data to index. Varada elastically adjusts the cluster to meet demand and optimize cost and performance.

Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used repeatedly by multiple teams and feature quality is critical to ensure a highly accurate model. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. SageMaker Feature Store provides a secured and unified store for feature use across the ML lifecycle. Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications. Ingest features from any data source including streaming and batch such as application logs, service logs, clickstreams, sensors, etc.

No more data catalog ghost towns. Acryl Cloud drives fast time-to-value via Shift Left practices for data producers and an intuitive UI for data consumers. Continuously detect data quality incidents in real-time, automate anomaly detection to prevent breakages, and drive fast resolution when they do occur. Acryl Cloud supports both push-based and pull-based metadata ingestion for easy maintenance, ensuring information is trustworthy, up-to-date, and definitive. Data should be operational. Go beyond simple visibility and use automated Metadata Tests to continuously expose data insights and surface new areas for improvement. Reduce confusion and accelerate resolution with clear asset ownership, automatic detection, streamlined alerts, and time-based lineage for tracing root causes.

Customer attention is scarce, and marketing and CX leaders are facing increasing pressure to continue driving growth with less time and tighter resources. With the limitless potential of generative AI, Pendula empowers teams to deliver adaptive and meaningful two-way customer experiences to inspire, engage, and retain customers at scale. Pendula aims to be at the heart of every conversation between businesses and their customers, all over the world. Intuitive drag-and-drop functionality that will empower teams to do their best work. Create two-way conversations and act on them intelligently with real-time data. Harness the power of AI to activate your entire data stack. The heart of next-gen customer engagement and retention. With Pendula's workflow studio, the only limits are the boundaries of your creative thinking. Select a data source to identify the moments that actually matter to your customers.

Databricks Unity Catalog is the industry’s only unified and open governance solution for data and AI, built into the Databricks Data Intelligence Platform. With Unity Catalog, organizations can seamlessly govern both structured and unstructured data in any format, as well as machine learning models, notebooks, dashboards, and files across any cloud or platform. Data scientists, analysts, and engineers can securely discover, access, and collaborate on trusted data and AI assets across platforms, leveraging AI to boost productivity and unlock the full potential of the lakehouse environment. This unified and open approach to governance promotes interoperability and accelerates data and AI initiatives while simplifying regulatory compliance. Easily discover and classify both structured and unstructured data in any format, including machine learning models, notebooks, dashboards, and files across all cloud platforms.

Amazon DataZone is a data management service that enables customers to catalog, discover, share, and govern data stored across AWS, on-premises, and third-party sources. It allows administrators and data stewards to manage and control access to data using fine-grained controls, ensuring that users have the appropriate level of privileges and context. The service simplifies data access for engineers, data scientists, product managers, analysts, and business users, facilitating data-driven insights through seamless collaboration. Key features of Amazon DataZone include a business data catalog for searching and requesting access to published data, project collaboration tools for managing and monitoring data assets, a web-based portal providing personalized views for data analytics, and governed data sharing workflows to ensure appropriate data access. Additionally, Amazon DataZone automates data discovery and cataloging using machine learning.

Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. Built on Amazon DataZone, it integrates various AWS analytics and AI/ML services, such as Amazon EMR, AWS Glue, and Amazon Bedrock, into a single platform. Users can discover, access, and process data from various sources like Amazon S3 and Redshift, and develop generative AI applications. With tools for model development, governance, MLOps, and AI customization, SageMaker Unified Studio provides an efficient, secure, and collaborative environment for data teams.

SDF is a developer platform for data that enhances SQL comprehension across organizations, enabling data teams to unlock the full potential of their data. It provides a transformation layer to streamline query writing and management, an analytical database engine for local execution, and an accelerator for improved transformation processes. SDF also offers proactive quality and governance features, including reports, contracts, and impact analysis, to ensure data integrity and compliance. By representing business logic as code, SDF facilitates the classification and management of data types, enhancing the clarity and maintainability of data models. It integrates seamlessly with existing data workflows, supporting various SQL dialects and cloud environments, and is designed to scale with the growing needs of data teams. SDF's open-core architecture, built on Apache DataFusion, allows for customization and extension, fostering a collaborative ecosystem for data development.