Best Data Management Software for GitLab - Page 2

Compare the Top Data Management Software that integrates with GitLab as of November 2024 - Page 2

This a list of Data Management software that integrates with GitLab. Use the filters on the left to add additional filters for products that have integrations with GitLab. View the products that work with GitLab in the table below.

  • 1
    Meltano

    Meltano

    Meltano

    Meltano provides the ultimate flexibility in deployment options. Own your data stack, end to end. Ever growing connector library of 300+ connectors have been running in production for years. Run workflows in isolated environments, execute end-to-end tests, and version control everything. Open source gives you the power to build your ideal data stack. Define your entire project as code and collaborate confidently with your team. The Meltano CLI enables you to rapidly create your project, making it easy to start replicating data. Meltano is designed to be the best way to run dbt to manage your transformations. Your entire data stack is defined in your project, making it simple to deploy it to production. Validate your changes in development before moving to CI, and in staging before moving to production.
  • 2
    Foundational

    Foundational

    Foundational

    Identify code and optimization issues in real-time, prevent data incidents pre-deploy, and govern data-impacting code changes end to end—from the operational database to the user-facing dashboard. Automated, column-level data lineage, from the operational database all the way to the reporting layer, ensures every dependency is analyzed. Foundational automates data contract enforcement by analyzing every repository from upstream to downstream, directly from source code. Use Foundational to proactively identify code and data issues, find and prevent issues, and create controls and guardrails. Foundational can be set up in minutes with no code changes required.
  • 3
    Nightfall

    Nightfall

    Nightfall

    Discover, classify, and protect your sensitive data. Nightfall™ uses machine learning to identify business-critical data, like customer PII, across your SaaS, APIs, and data infrastructure, so you can manage & protect it. Integrate in minutes with cloud services via APIs to monitor data without agents. Machine learning classifies your sensitive data & PII with high accuracy, so nothing gets missed. Setup automated workflows for quarantines, deletions, alerts, and more - saving you time and keeping your business safe. Nightfall integrates directly with all your SaaS, APIs, and data infrastructure. Start building with Nightfall’s APIs for sensitive data classification & protection for free. Via REST API, programmatically get structured results from Nightfall’s deep learning-based detectors for things like credit card numbers, API keys, and more. Integrate with just a few lines of code. Seamlessly add data classification to your applications & workflows using Nightfall's REST API.
  • 4
    HPE Ezmeral

    HPE Ezmeral

    Hewlett Packard Enterprise

    Run, manage, control and secure the apps, data and IT that run your business, from edge to cloud. HPE Ezmeral advances digital transformation initiatives by shifting time and resources from IT operations to innovations. Modernize your apps. Simplify your Ops. And harness data to go from insights to impact. Accelerate time-to-value by deploying Kubernetes at scale with integrated persistent data storage for app modernization on bare metal or VMs, in your data center, on any cloud or at the edge. Harness data and get insights faster by operationalizing the end-to-end process to build data pipelines. Bring DevOps agility to the machine learning lifecycle, and deliver a unified data fabric. Boost efficiency and agility in IT Ops with automation and advanced artificial intelligence. And provide security and control to eliminate risk and reduce costs. HPE Ezmeral Container Platform provides an enterprise-grade platform to deploy Kubernetes at scale for a wide range of use cases.
  • 5
    Datafold

    Datafold

    Datafold

    Prevent data outages by identifying and fixing data quality issues before they get into production. Go from 0 to 100% test coverage of your data pipelines in a day. Know the impact of each code change with automatic regression testing across billions of rows. Automate change management, improve data literacy, achieve compliance, and reduce incident response time. Don’t let data incidents take you by surprise. Be the first one to know with automated anomaly detection. Datafold’s easily adjustable ML model adapts to seasonality and trend patterns in your data to construct dynamic thresholds. Save hours spent on trying to understand data. Use the Data Catalog to find relevant datasets, fields, and explore distributions easily with an intuitive UI. Get interactive full-text search, data profiling, and consolidation of metadata in one place.
  • 6
    OpsHub

    OpsHub

    OpsHub

    OpsHub Integration Manager (OIM) can be configured to synchronize data between any of the 50+ tools in the ALM ecosystem. OIM provides an easy-to-use interface and intuitive user experience allowing users to easily configure the integration. The platform is built to be resilient and guarantees consistency of data in the systems that are being integrated. Businesses with heterogeneous IT landscapes need an agile integration that can put their entire value stream on a fast track and be a partner in their digital transformation. To remain competitive in the ever-evolving digital economy, it is now more crucial than ever to optimize processes and keep each step through the process connected. With OpsHub, get an enterprise-class integration solution that has been transforming clients’ value stream for over two decades.
  • 7
    Carbide

    Carbide

    Carbide

    Get compliant, prevent breaches, and save money with a security and privacy program that doesn’t slow down your growth. While “checkbox”-style security and privacy can seem attractive, it builds security debt that multiplies with each new regulation and every new security questionnaire. Instead, Carbide makes enterprise-class security accessible to companies of all sizes. That means that start-ups get the step-by-step support they need to design and implement strong security and privacy, while established security teams gain back valuable time by capitalizing on the automation and efficiency provided by the platform. Adopting a security and privacy posture that goes beyond checkbox compliance is possible even without a large security team. Carbide breaks down enterprise-class security and privacy requirements and makes them accessible to, and achievable by, companies of all sizes.
  • 8
    Benerator

    Benerator

    Benerator

    Describe your data model on an abstract level in XML. Involve your business people as no developer skills are necessary. Use a wide range of function libraries to fake realistic data. Write your own extensions in Javascript or Java. Integrate your data processes into Gitlab CI or Jenkins. Generate, anonymize, and migrate with Benerator’s model-driven data toolkit. Define processes to anonymize or pseudonymize data in plain XML on an abstract level without the need for developer skills. Stay GDPR compliant with your data and protect the privacy of your customers. Mask and obfuscate sensitive data for BI, test, development, or training purposes. Combine data from various sources (subsetting) and keep the data integrity. Migrate and transform your data in multisystem landscapes. Reuse your testing data models to migrate production environments. Keep your data consistent and reliable in a microsystem architecture.
  • 9
    MINDely
    MIND is the first-ever data security platform that puts data loss prevention (DLP) and insider risk management (IRM) programs on autopilot, so you can automatically identify, detect, and prevent data leaks at machine speed. Continuously find your sensitive data in files spread across your IT environments whether at rest, in motion, or in use. MIND continuously exposes blindspots of sensitive data across your IT environments including SaaS, AI apps, endpoints, on-premise file shares, and emails. MIND monitors and analyzes billions of data security events in real time, enriches each incident with context, and remediates autonomously. MIND automatically blocks sensitive data in real-time from escaping your control, or collaborates with users to remediate risks and educate on your policies. MIND continuously exposes blindspots of sensitive data at rest, in motion, and in use by integrating with data sources across your IT workloads, e.g. SaaS, AI apps, on-premises, endpoints, and emails.
  • 10
    Singer

    Singer

    Singer

    Singer describes how data extraction scripts called “taps” and data loading scripts called “targets” should communicate, allowing them to be used in any combination to move data from any source to any destination. Send data between databases, web APIs, files, queues, and just about anything else you can think of. Singer taps and targets are simple applications composed with pipes—no daemons or complicated plugins needed. Singer applications communicate with JSON, making them easy to work with and implement in any programming language. Singer also supports JSON Schema to provide rich data types and rigid structure when needed. Singer makes it easy to maintain state between invocations to support incremental extraction.