Amazon EMR Integrations

There are an estimated 25 million engineers in the world across dozens of distinct functions. As every company becomes a software company, engineers are using New Relic to gather real-time insights and trending data about the performance of their software so they can be more resilient and deliver exceptional customer experiences. Only New Relic provides an all-in-one platform that is built and sold as a unified experience. With New Relic, customers get access to a secure telemetry cloud for all metrics, events, logs, and traces; powerful full-stack analysis tools; and simple, transparent usage-based pricing with only 2 key metrics. New Relic has also curated one of the industry’s largest ecosystems of open source integrations, making it easy for every engineer to get started with observability and use New Relic alongside their other favorite applications.

Service Center by Office Ally is a trusted revenue cycle management platform used by over 65,000 healthcare organizations processing more than 350 million claims annually. With it, providers can verify patient eligibility and benefits, upload and submit claims, correct rejected claims, check claim status, and obtain remits. With multiple claim types and submission options, providers can easily submit claims to any payer from any practice management system. Transactions are secure, ensuring the confidentiality of sensitive patient information. With no needed implementation, providers can quickly and effortlessly streamline their billing processes, increase their financial performance, simplify medical billing, and reduce claim rejections for faster reimbursements.

The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive. Apache Hive is an open source project run by volunteers at the Apache Software Foundation. Previously it was a subproject of Apache® Hadoop®, but has now graduated to become a top-level project of its own. We encourage you to learn about the project and contribute your expertise. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API.

Immuta is the market leader in secure Data Access, providing data teams one universal platform to control access to analytical data sets in the cloud. Only Immuta can automate access to data by discovering, securing, and monitoring data. Data-driven organizations around the world trust Immuta to speed time to data, safely share more data with more users, and mitigate the risk of data leaks and breaches. Founded in 2015, Immuta is headquartered in Boston, MA. Immuta is the fastest way for algorithm-driven enterprises to accelerate the development and control of machine learning and advanced analytics. The company's hyperscale data management platform provides data scientists with rapid, personalized data access to dramatically improve the creation, deployment and auditability of machine learning and AI.

AWS Step Functions is a serverless function orchestrator that makes it easy to sequence AWS Lambda functions and multiple AWS services into business-critical applications. Through its visual interface, you can create and run a series of checkpointed and event-driven workflows that maintain the application state. The output of one step acts as an input to the next. Each step in your application executes in order, as defined by your business logic. Orchestrating a series of individual serverless applications, managing retries, and debugging failures can be challenging. As your distributed applications become more complex, the complexity of managing them also grows. With its built-in operational controls, Step Functions manages sequencing, error handling, retry logic, and state, removing a significant operational burden from your team. AWS Step Functions lets you build visual workflows that enable fast translation of business requirements into technical requirements.

Our platform allows businesses to use data—including its application in advanced analytics, machine learning, and AI—to do great things without worrying about putting customers, employees, or intellectual property at risk. The Protegrity Data Protection Platform doesn't just secure data—it simultaneously classifies and discovers data while protecting it. You can't protect what you don't know you have. Our platform first classifies data, allowing users to categorize the type of data that can mostly be in the public domain. With those classifications established, the platform then leverages machine learning algorithms to discover that type of data. Classification and discovery finds the data that needs to be protected. Whether encrypting, tokenizing, or applying privacy methods, the platform secures the data behind the many operational systems that drive the day-to-day functions of business, as well as the analytical systems behind decision-making.

Ataccama reinvents the way data is managed to create value on an enterprise scale. Unifying Data Governance, Data Quality, and Master Data Management into a single, AI-powered fabric across hybrid and Cloud environments, Ataccama gives your business and data teams the ability to innovate with unprecedented speed while maintaining trust, security, and governance of your data.

AWS Data Pipeline is a web service that helps you reliably process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals. With AWS Data Pipeline, you can regularly access your data where it’s stored, transform and process it at scale, and efficiently transfer the results to AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR. AWS Data Pipeline helps you easily create complex data processing workloads that are fault tolerant, repeatable, and highly available. You don’t have to worry about ensuring resource availability, managing inter-task dependencies, retrying transient failures or timeouts in individual tasks, or creating a failure notification system. AWS Data Pipeline also allows you to move and process data that was previously locked up in on-premises data silos.

CopperEgg provides essential monitoring capabilities, giving you everything you need to identify and react to cloud infrastructure issues – from user experience all the way to the database. Understanding the complex nature of today’s IT infrastructures, we include out-of-the-box and fully customizable dashboards, alerts, and management reports to match the needs of your particular environment. The CopperEgg Apdex rating is a composite of performance metrics with a comparison to historical norms and alerts you with red, yellow, and green health indicators. So if your server performance suddenly increases above its typical norm, the Apdex rating will be a good sign that something wrong. The Apdex rating is calculated through an algorithm that weights key health indicators, such as response time, server CPU, disk IO, memory, and more against a baseline trend.

Prophecy enables many more users - including visual ETL developers and Data Analysts. All you need to do is point-and-click and write a few SQL expressions to create your pipelines. As you use the Low-Code designer to build your workflows - you are developing high quality, readable code for Spark and Airflow that is committed to your Git. Prophecy gives you a gem builder - for you to quickly develop and rollout your own Frameworks. Examples are Data Quality, Encryption, new Sources and Targets that extend the built-in ones. Prophecy provides best practices and infrastructure as managed services – making your life and operations simple! With Prophecy, your workflows are high performance and use scale-out performance & scalability of the cloud.

AWS App Mesh is a service mesh that provides application-level networking to facilitate communication between your services across various types of computing infrastructure. App Mesh offers comprehensive visibility and high availability for your applications. Modern applications are generally made up of multiple services. Each service can be developed using various types of compute infrastructure, such as Amazon EC2, Amazon ECS, Amazon EKS, and AWS Fargate. As the number of services within an application grows, it becomes difficult to pinpoint the exact location of errors, redirect traffic after errors, and safely implement code changes. Previously, this required creating monitoring and control logic directly in your code and redeploying your services every time there were changes.

Data is being reconstructed for the cloud. Identity has taken a new definition beyond just humans, extending to service accounts and principals. Authorization is the truest form of identity. The multi-cloud world requires a novel, dynamic approach to secure enterprise data. Only Veza can give you a comprehensive view of authorization across your identity-to-data relationships. Veza is a cloud-native, agentless platform, and introduces no risk to your data or its availability. We make it easy for you to manage authorization across your entire cloud ecosystem so you can empower your users to share data securely. Veza supports the most common critical systems from day one — unstructured data systems, structured data systems, data lakes, cloud IAM, and apps — and makes it possible for you to bring your own custom apps by leveraging Veza’s Open Authorization API.

Stop wasting time provisioning and maintaining databases yourself. Effortlessly create isolated test databases to ship features faster. Equip your developers with the ready-to-go data they need to keep fast-paced projects on track. Spin up pre-populated databases for testing purposes as part of your CI/CD pipeline, and automatically tear them down once the tests are done. Quickly and painlessly spin up databases at the click of a button for testing, bug reproduction, demos, and more with built-in container orchestration. Use our patented subsetter to shrink PBs down to GBs without breaking referential integrity, then leverage Tonic Ephemeral to spin up a database with only the data needed for development to cut cloud costs and maximize efficiency. Pair our patented subsetted with Tonic Ephemeral to get all the data subsets you need for only as long as you need them. Maximize efficiency by getting your developers access to one-off datasets for local development.

Connect and centralize data. Transform your existing data landscape into a flexible data powerhouse. Data Virtuality is a data integration platform for instant data access, easy data centralization and data governance. Our Logical Data Warehouse solution combines data virtualization and materialization for the highest possible performance. Build your single source of data truth with a virtual layer on top of your existing data environment for high data quality, data governance, and fast time-to-market. Hosted in the cloud or on-premises. Data Virtuality has 3 modules: Pipes, Pipes Professional, and Logical Data Warehouse. Cut down your development time by up to 80%. Access any data in minutes and automate data workflows using SQL. Use Rapid BI Prototyping for significantly faster time-to-market. Ensure data quality for accurate, complete, and consistent data. Use metadata repositories to improve master data management.

Powering management to drive business performance. Management is slow, in-person and fragmented, making rapid, data-driven collaboration difficult. Quorso joins up management in a single tool – connecting your KPIs to your data, team and actions to power business performance. Create KPIs in seconds, then sit back as Quorso hunts through your data, finding actionable insights for each team member. Quorso helps your team deliver each action, then measures the impact so everyone learns what works. Quorso helps you remotely manage, engage and collaborate with your team – so it feels like you are on site every day. Quorso shows you how every action by every team member is improving your KPIs. Quorso boosts management productivity across every area of your business.

Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices. You can use Spot Instances for various stateless, fault-tolerant, or flexible applications such as big data, containerized workloads, CI/CD, web servers, high-performance computing (HPC), and test & development workloads. Because Spot Instances are tightly integrated with AWS services such as Auto Scaling, EMR, ECS, CloudFormation, Data Pipeline and AWS Batch, you can choose how to launch and maintain your applications running on Spot Instances. Moreover, you can easily combine Spot Instances with On-Demand, RIs and Savings Plans Instances to further optimize workload cost with performance. Due to the operating scale of AWS, Spot Instances can offer the scale and cost savings to run hyper-scale workloads.

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™.

Democratize, collaborate, and operationalize, machine learning across your organization. Data science is a team sport. Data scientists, citizen data scientists, data engineers, business users, and developers need flexible and extensible tools that promote collaboration, automation, and reuse of analytic workflows. But algorithms are only one piece of the advanced analytic puzzle. To deliver predictive insights, companies need to increase focus on the deployment, management, and monitoring of analytic models. Smart businesses rely on platforms that support the end-to-end analytics lifecycle while providing enterprise security and governance. TIBCO® Data Science software helps organizations innovate and solve complex problems faster to ensure predictive findings quickly turn into optimal outcomes. TIBCO Data Science allows organizations to expand data science deployments across the organization by providing flexible authoring and deployment capabilities.

Okera, the Universal Data Authorization company, helps modern, data-driven enterprises accelerate innovation, minimize data security risks, and demonstrate regulatory compliance. The Okera Dynamic Access Platform automatically enforces universal fine-grained access control policies. This allows employees, customers, and partners to use data responsibly, while protecting them from inappropriately accessing data that is confidential, personally identifiable, or regulated. Okera’s robust audit capabilities and data usage intelligence deliver the real-time and historical information that data security, compliance, and data delivery teams need to respond quickly to incidents, optimize processes, and analyze the performance of enterprise data initiatives. Okera began development in 2016 and now dynamically authorizes access to hundreds of petabytes of sensitive data for the world’s most demanding F100 companies and regulatory agencies. The company is headquartered in San Francisco.

Whether you want to build a governed delta lake, data warehouse, or simply want to migrate from your traditional database to a modern cloud data warehouse, do it all with Lyftrondata. Simply create and manage all of your data workloads on one platform by automatically building your pipeline and warehouse. Analyze it instantly with ANSI SQL, BI/ML tools, and share it without worrying about writing any custom code. Boost the productivity of your data professionals and shorten your time to value. Define, categorize, and find all data sets in one place. Share these data sets with other experts with zero codings and drive data-driven insights. This data sharing ability is perfect for companies that want to store their data once, share it with other experts, and use it multiple times, now and in the future. Define dataset, apply SQL transformations or simply migrate your SQL data processing logic to any cloud data warehouse.

Deploy machine learning applications to production in minutes, rather than months. Automate the transformation of raw data, generate training data sets, and serve features for online inference at scale. Save months of work by replacing bespoke data pipelines with robust pipelines that are created, orchestrated and maintained automatically. Increase your team’s efficiency by sharing features across the organization and standardize all of your machine learning data workflows in one platform. Serve features in production at extreme scale with the confidence that systems will always be up and running. Tecton meets strict security and compliance standards. Tecton is not a database or a processing engine. It plugs into and orchestrates on top of your existing storage and processing infrastructure.

Make your offline data available for real-time predictions without having to build custom pipelines. Ensure data consistency between offline training and online inference, eliminating train-serve skew. Standardize data engineering workflows under one consistent framework. Teams use Feast as the foundation of their internal ML platforms. Feast doesn’t require the deployment and management of dedicated infrastructure. Instead, it reuses existing infrastructure and spins up new resources when needed. You are not looking for a managed solution and are willing to manage and maintain your own implementation. You have engineers that are able to support the implementation and management of Feast. You want to run pipelines that transform raw data into features in a separate system and integrate with it. You have unique requirements and want to build on top of an open source solution.

Empowering applications with enterprise data is our passion here at Progress DataDirect. We offer cloud and on-premises data connectivity solutions across relational, NoSQL, Big Data, and SaaS data sources. Performance, reliability, and security are at the heart of everything we design for thousands of enterprises and the leading vendors in analytics, BI, and data management. Minimize your development costs with our portfolio of high-value connectors for a variety of data sources. Enjoy 24/7 world-class support and security for greater peace of mind. Connect with affordable, easy-to-use, and time-saving drivers for faster SQL access to your data. As a leader in data connectivity, keeping up with the evolving trends in space is our mission. But if we haven’t built the connector you need yet, reach out and we’ll help you develop the right solution. Embed connectivity in an application or service.

The TrustLogix Cloud Data Security Platform breaks down silos between data owners, security owners, and data consumers with simplified data access management and compliance. Discover cloud data access issues and risks in 30 minutes or less, without requiring visibility to the data itself. Deploy fine-grained attribute-based access control (ABAC) and role-based access control (RBAC) policies and centrally manage your data security posture across all clouds and data platforms. TrustLogix continuously monitors and alerts for new risks and non-compliance such as suspicious activity, over-privileged accounts, ghost accounts, and new dark data or data sprawl, thus empowering you to respond quickly and decisively to address them. Additionally, alerts can be reported to SIEM and other GRC systems.

The Saagie cloud data factory is a turnkey platform that lets you create and manage all your data & AI projects in a single interface, deployable in just a few clicks. Develop your use cases and test your AI models in a secure way with the Saagie data factory. Get your data and AI projects off the ground with a single interface and centralize your teams to make rapid progress. Whatever your maturity level, from your first data project to a data & AI-driven strategy, the Saagie platform is there for you. Simplify your workflows, boost your productivity, and make more informed decisions by unifying your work on a single platform. Transform your raw data into powerful insights by orchestrating your data pipelines. Get quick access to the information you need to make more informed decisions. Simplify the management and scalability of your data and AI infrastructure. Accelerate the time-to-production of your AI, machine learning, and deep learning models.

Unravel makes data work anywhere: on Azure, AWS, GCP or in your own data center– Optimizing performance, automating troubleshooting and keeping costs in check. Unravel helps you monitor, manage, and improve your data pipelines in the cloud and on-premises – to drive more reliable performance in the applications that power your business. Get a unified view of your entire data stack. Unravel collects performance data from every platform, system, and application on any cloud then uses agentless technologies and machine learning to model your data pipelines from end to end. Explore, correlate, and analyze everything in your modern data and cloud environment. Unravel’s data model reveals dependencies, issues, and opportunities, how apps and resources are being used, what’s working and what’s not. Don’t just monitor performance – quickly troubleshoot and rapidly remediate issues. Leverage AI-powered recommendations to automate performance improvements, lower costs, and prepare.

Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Automatic failover support between RegionServers. Easy to use Java API for client access. Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options. Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX.

Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. For data engineers who struggle with managing multiple query languages and interfaces to siloed databases and storage, Presto is the fast and reliable engine that provides one simple ANSI SQL interface for all your data analytics and your open lakehouse. Different engines for different workloads means you will have to re-platform down the road. With Presto, you get 1 familar ANSI SQL language and 1 engine for your data analytics so you don't need to graduate to another lakehouse engine. Presto can be used for interactive and batch workloads, small and large amounts of data, and scales from a few to thousands of users. Presto gives you one simple ANSI SQL interface for all of your data in various siloed data systems, helping you join your data ecosystem together.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. A wide variety of companies and organizations use Hadoop for both research and production. Users are encouraged to add themselves to the Hadoop PoweredBy wiki page. Apache Hadoop 3.3.4 incorporates a number of significant enhancements over the previous major release line (hadoop-3.2).

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Monitor your data health and pipeline performance. Gain unified visibility for pipelines running on cloud-native tools like Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. An observability platform purpose built for Data Engineers. Data engineering is only getting more challenging as demands from business stakeholders grow. Databand can help you catch up. More pipelines, more complexity. Data engineers are working with more complex infrastructure than ever and pushing higher speeds of release. It’s harder to understand why a process has failed, why it’s running late, and how changes affect the quality of data outputs. Data consumers are frustrated with inconsistent results, model performance, and delays in data delivery. Not knowing exactly what data is being delivered, or precisely where failures are coming from, leads to persistent lack of trust. Pipeline logs, errors, and data quality metrics are captured and stored in independent, isolated systems.

Data science driven security controls to automate advanced threat detection, remediation and response. Gurucul’s Unified Security and Risk Analytics platform answers the question: Is anomalous behavior risky? This is our competitive advantage and why we’re different than everyone else in this space. We don’t waste your time with alerts on anomalous activity that isn’t risky. We use context to determine whether behavior is risky. Context is critical. Telling you what’s happening is not helpful. Telling you when something bad is happening is the Gurucul difference. That’s information you can act on. We put your data to work. We are the only security analytics company that can consume all your data out-of-the-box. We can ingest data from any source – SIEMs, CRMs, electronic medical records, identity and access management systems, end points – you name it, we ingest it into our enterprise risk engine.

AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis. A data lake lets you break down data silos and combine different types of analytics to gain insights and guide better business decisions. Setting up and managing data lakes today involves a lot of manual, complicated, and time-consuming tasks. This work includes loading data from diverse sources, monitoring those data flows, setting up partitions, turning on encryption and managing keys, defining transformation jobs and monitoring their operation, reorganizing data into a columnar format, deduplicating redundant data, and matching linked records. Once data has been loaded into the data lake, you need to grant fine-grained access to datasets, and audit access over time across a wide range of analytics and machine learning (ML) tools and services.

Built by engineers for engineers, our Prometheus monitoring tool is easy to configure, get set up, and begin sending metrics. We take care of scaling your Prometheus, so you don't need to worry about it. We keep your data long-term, with 3x redundancy, so you can focus on applying the data rather than maintaining a database. Get updates and plugins without lifting a finger, as we keep your Prometheus and Grafana stack updated for you. Everything you need to take control of your Prometheus metrics. Vendor lock-in's not our thing. We’re believers in you still owning your data, so you can request a full export at any time. That means you get all the benefits of an open-source tool, but with the security and stability of a SaaS tool. We keep all your data with 3 times the redundancy and keep your data in a safe place for up to 1 year. Scale without fear, we handle all the hassle for you. Prometheus experts are available 24 hours a day.

Sync, search and manage all the work across your data science team. Zepl’s powerful search lets you discover and reuse models and code. Use Zepl’s enterprise collaboration platform to query data from Snowflake, Athena or Redshift and build your models in Python. Use pivoting and dynamic forms for enhanced interactions with your data using heatmap, radar, and Sankey charts. Zepl creates a new container every time you run your notebook, providing you with the same image each time you run your models. Invite team members to join a shared space and work together in real time or simply leave their comments on a notebook. Use fine-grained access controls to share your work. Allow others have read, edit, and run access as well as enable collaboration and distribution. All notebooks are auto-saved and versioned. You can name, manage and roll back all versions through an easy-to-use interface, and export seamlessly into Github.

Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset.

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. Quickly move between steps of the ML lifecycle to fine-tune your models. Replay training experiments, tune model features and other inputs, and compare results, without leaving SageMaker Studio.

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data you want from a wide variety of data sources and import it quickly. Next, you can use the Data Quality and Insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly transform data without writing any code. Once you have completed your data preparation workflow, you can scale it to your full datasets using SageMaker data processing jobs; train, tune, and deploy models.

Monitor and control everything your data pipelines do with zero code changes. Monitor data and pipelines in motion to proactively prevent downtime and quickly root cause issues. Optimize pipeline runs and job performance to save costs and keep SLAs. Accelerate code deployments and platform upgrades while maintaining reliability and performance. Data & performance checks in line with pipeline runs. Checks on input data, before pipelines even run. Automatic preemption of runs. definity takes away the effort to build deep end-to-end coverage, so you are protected at every step, across every dimension. definity shifts observability to post-production to achieve ubiquity, increase coverage, and reduce manual effort. definity agents automatically run with every pipeline, with zero footprints. Unified view of data, pipelines, infra, lineage, and code for every data asset. Detect in run-time and avoid async checks. Auto-preempt runs, even on inputs.