Business Software for Hadoop

Bacula Enterprise offers a single platform that provides cloud backup and recovery software for the Modern Data Center. Bacula Enterprise backup & recovery software is ideal for medium and large businesses. It offers unique innovation, modern architecture and business value benefits, as well as low cost of ownership. Bacula Enterprise corporate backup software solution uses unique technologies that increase the interoperability of Bacula Enterprise into many IT environments, such as managed service providers, software vendors, cloud providers, enterprise data centers, and cloud providers. Bacula Enterprise is used by thousands of organizations around the world in mission-critical environments such as NASA, Texas A&M University and Unicredit. Bacula offers more security features than other vendors and advanced hybrid Cloud connectivity to Amazon S3, Google, Oracle, and many others.

IBM® StreamSets allows users to create and maintain smart streaming data pipelines using an intuitive graphical user interface. This facilitates seamless data integration in hybrid and multicloud environments. IBM StreamSets is used by leading global companies to support millions data pipelines, for modern analytics and intelligent applications. Reduce data staleness, and enable real-time information at scale. Handle millions of records across thousands of pipelines in seconds. Drag-and-drop processors that automatically detect and adapt to data drift will protect your data pipelines against unexpected changes and shifts. Create streaming pipelines for ingesting structured, semistructured, or unstructured data to deliver it to multiple destinations.

Enhance your metrics and alerting capabilities using a top-tier open-source monitoring tool. Prometheus inherently organizes all data as time series, which consist of sequences of timestamped values associated with the same metric and a specific set of labeled dimensions. In addition to the stored time series, Prometheus has the capability to create temporary derived time series based on query outcomes. The tool features a powerful query language known as PromQL (Prometheus Query Language), allowing users to select and aggregate time series data in real time. The output from an expression can be displayed as a graph, viewed in tabular format through Prometheus’s expression browser, or accessed by external systems through the HTTP API. Configuration of Prometheus is achieved through a combination of command-line flags and a configuration file, where the flags are used to set immutable system parameters like storage locations and retention limits for both disk and memory. This dual method of configuration ensures a flexible and tailored monitoring setup that can adapt to various user needs. For those interested in exploring this robust tool, further details can be found at: https://sourceforge.net/projects/prometheus.mirror/

Enterprise Recon by Ground Labs is a leading, award-winning solution that empowers organizations to confidently discover, manage, and remediate sensitive personal data across their entire digital estate—from legacy systems to the modern cloud. Our technology provides the unparalleled visibility needed to reduce risk, simplify compliance, and maintain a strong security posture globally. Unmatched Discovery and Accuracy Powered by GLASS™ At the core of Enterprise Recon is GLASS Technology™, Ground Labs' proprietary pattern-matching engine. This is a crucial differentiator, designed specifically for data discovery: Fastest and Most Accurate: GLASS Technology™ allows Enterprise Recon to deliver the fastest and most accurate sensitive data discovery on the market, dramatically minimizing system overheads and the most common complaint in the industry: false positives. Deep Search Capabilities: It performs sophisticated, deep searches for over 300 pre-configured, out-of-the-box data types across various formats, including databases, documents, emails, compressed files, and even in-memory data, ensuring no sensitive asset is missed. Customization: Enables complete customisation of sensitive data types, enabling organizations to search for proprietary or highly-specific data patterns unique to their business or industry. Comprehensive Platform and Deployment Coverage Enterprise Recon is engineered for the complex, heterogeneous environments of the modern enterprise, offering unparalleled breadth in platform support: Broad OS Support: Supports sensitive data discovery on an extensive range of operating systems, including common platforms like Windows, macOS, and Linux, as well as legacy and specialized systems such as FreeBSD, Solaris and AIX

IRI Data Masking as a Service is a professional services engagement to secure PII. Step 1: IRI agrees under NDA terms to classify, analyze, and report on the sensitive, at-risk data in your sources. We will discuss an initial cost estimate then hone it with you during data discovery. Step 2: Transfer the unprotected data to a secure on-premise or cloud-based staging area or provide remote, supervised access to IRI to the data sources(s) at issue. We'll use the tools in the award-winning IRI Data Protector suite to mask that data according to your business rules, on an ad hoc or recurring basis. Step 3: Our experts can also move newly-masked data to incremental replicas in production or to lower non-production environments. From either, the data is now safe for analytic initiatives, development, testing, or training. Tell us if you need additional services, like re-ID risk scoring (expert determination) of the de-identified data. This approach provides the benefits of proven data masking solution technology and services without the need to learn and customize new software from scratch. If you do want to use the software in-house, you will have everything pre-configured for easier long-term self-use and modification.

Quobyte’s high-performance file and object storage provides the flexibility to be deployed across any server or cloud environment, enabling you to scale performance and efficiently manage substantial data volumes while streamlining administration tasks. Designed with the primary objective of enhancing user convenience, Quobyte simplifies storage management through a hassle-free installation process that avoids complex configurations and kernel module requirements. This flexibility in deployment allows you to choose the optimal environment for your storage solution, whether on new or existing hardware, in a cloud setup, or through a hybrid approach that suits your specific requirements. With Quobyte, all operations, including software updates and node management, are executed in a completely non-disruptive manner, allowing you to maintain productivity without interruptions. This means you can finally say goodbye to inconvenient maintenance windows, enabling you to reclaim your evenings and weekends for personal pursuits. Ultimately, Quobyte empowers you to focus on your core activities while ensuring your data storage needs are met seamlessly and efficiently.

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IBM Analytics Engine offers a unique architecture for Hadoop clusters by separating the compute and storage components. Rather than relying on a fixed cluster with nodes that serve both purposes, this engine enables users to utilize an object storage layer, such as IBM Cloud Object Storage, and to dynamically create computing clusters as needed. This decoupling enhances the flexibility, scalability, and ease of maintenance of big data analytics platforms. Built on a stack that complies with ODPi and equipped with cutting-edge data science tools, it integrates seamlessly with the larger Apache Hadoop and Apache Spark ecosystems. Users can define clusters tailored to their specific application needs, selecting the suitable software package, version, and cluster size. They have the option to utilize the clusters for as long as necessary and terminate them immediately after job completion. Additionally, users can configure these clusters with third-party analytics libraries and packages, and leverage IBM Cloud services, including machine learning, to deploy their workloads effectively. This approach allows for a more responsive and efficient handling of data processing tasks.

Leverage the most extensively utilized observability platform, founded on the reliable Elastic Stack (commonly referred to as the ELK Stack), to integrate disparate data sources, providing cohesive visibility and actionable insights. To truly monitor and extract insights from your distributed systems, it is essential to consolidate all your observability data within a single framework. Eliminate data silos by merging application, infrastructure, and user information into a holistic solution that facilitates comprehensive observability and alerting. By integrating limitless telemetry data collection with search-driven problem-solving capabilities, you can achieve superior operational and business outcomes. Unify your data silos by assimilating all telemetry data, including metrics, logs, and traces, from any source into a platform that is open, extensible, and scalable. Enhance the speed of problem resolution through automatic anomaly detection that leverages machine learning and sophisticated data analytics, ensuring you stay ahead in today's fast-paced environment. This integrated approach not only streamlines processes but also empowers teams to make informed decisions swiftly.

Dataplane's goal is to make it faster and easier to create a data mesh. It has robust data pipelines and automated workflows that can be used by businesses and teams of any size. Dataplane is more user-friendly and places a greater emphasis on performance, security, resilience, and scaling.

Our platform for data discovery and scanning operates without the need for agents, making it simple to integrate with any cloud accounts, including AWS, Azure, and GCP. You won't have to handle any deployments or management tasks. We are compatible with all native cloud data repositories, whether structured or unstructured, across these three major cloud providers. Normalyze efficiently scans both types of data within your cloud environments, collecting only metadata to enhance the Normalyze graph, ensuring that no sensitive information is gathered during the process. The platform visualizes access and trust relationships in real-time, offering detailed context that encompasses fine-grained process names, data store fingerprints, and IAM roles and policies. It enables you to swiftly identify all data stores that may contain sensitive information, uncover every access path, and evaluate potential breach paths according to factors like sensitivity, volume, and permissions, highlighting vulnerabilities that could lead to data breaches. Furthermore, the platform allows for the categorization and identification of sensitive data according to industry standards, including PCI, HIPAA, and GDPR, providing comprehensive compliance support. This holistic approach not only enhances data security but also empowers organizations to maintain regulatory compliance efficiently.

Superblocks is an enterprise platform designed to build and govern AI-generated applications using company data. It enables business teams to create production-ready apps without deep technical expertise. The platform integrates with major data systems such as Snowflake, Databricks, AWS, Google Cloud, and Azure. IT teams maintain centralized control over authentication, permissions, integrations, and auditing. Superblocks acts as a secure integration layer, ensuring apps do not directly access sensitive systems. It provides real-time monitoring of app usage, permissions, and potential vulnerabilities. The platform also enforces policies for security, compliance, and code standards across all applications. Users can deploy apps in cloud, hybrid, or private VPC environments based on security needs. Superblocks helps organizations replace legacy systems and accelerate internal tool development. Overall, it enables scalable, secure, and governed AI app creation across enterprise teams.

Dialogic OnDemand Voicemail operates entirely on software and is capable of functioning within virtualized settings, which facilitates resource sharing and lowers the costs associated with service delivery. It effectively reduces the necessity for multiple mailboxes by generating temporary resources that can be utilized by various subscribers while ensuring the same levels of privacy and security as traditional mailboxes. Unlike outdated systems that incur high maintenance expenses and demand additional physical space and power, transitioning to a fully virtualized, on-demand platform can significantly decrease operational costs without sacrificing service quality. The user-friendly interface is specifically crafted to boost your subscribers’ ability to manage their own services, ultimately leading to a reduction in customer care expenses as well. This system allows for the creation of dynamic and temporary voicemail boxes, assigning them to customers only when required. By minimizing the number of voicemail boxes, costs can be reduced further, and accessibility is granted from any device at any location. Not only does this upgrade enhance the visual appeal of your voicemail service, but it also ensures customers benefit from the latest features simultaneously. Furthermore, the flexibility of the system fosters a more responsive service tailored to user needs.

muCommander is a versatile, open-source file management tool with a dual-pane layout that works seamlessly across all major operating systems. It offers functionality such as copying, moving, renaming, and batch renaming files, as well as the ability to email them. Users can take advantage of multiple tabs and universal bookmarks for enhanced organization, along with a credentials manager to securely store access information. The application allows for customizable keyboard shortcuts for improved efficiency and supports integration with cloud storage services like Dropbox and Google Drive. Its virtual filesystem is capable of handling local volumes and various protocols including FTP, SFTP, SMB, NFS, HTTP, Amazon S3, Hadoop HDFS, and Bonjour. Additionally, muCommander can manage archives in formats such as ZIP, RAR, 7z, TAR, GZip, BZip2, ISO/NRG, and AR/Deb, while providing checksum calculations for file verification. The user interface is fully customizable, allowing users to adjust toolbars and themes to their liking, and it is available in a variety of languages. Notably, muCommander is a lightweight, cross-platform file manager that requires Java 11 or later for operation. Users are encouraged to report bugs, propose new features, answer inquiries, contribute to documentation, create video tutorials, or assist in translating the user interface. To begin using Open Office with muCommander, simply open the document in a "native" manner, which is mapped to shift-enter by default.

Traditional Data Lakes frequently simplify their role to merely serving as inexpensive raw data repositories, overlooking crucial elements such as data transformation, quality assurance, and security protocols. Consequently, data scientists often find themselves dedicating as much as 80% of their time to the processes of data acquisition, comprehension, and cleansing, which delays their ability to leverage their primary skills effectively. Furthermore, the establishment of traditional Data Lakes tends to occur in isolation by various departments, each utilizing different standards and tools, complicating the implementation of cohesive analytical initiatives. In contrast, Smart Data Lakes address these challenges by offering both architectural and methodological frameworks, alongside a robust toolset designed to create a high-quality data infrastructure. Essential to any contemporary analytics platform, Smart Data Lakes facilitate seamless integration with popular Data Science tools and open-source technologies, including those used for artificial intelligence and machine learning applications. Their cost-effective and scalable storage solutions accommodate a wide range of data types, including unstructured data and intricate data models, thereby enhancing overall analytical capabilities. This adaptability not only streamlines operations but also fosters collaboration across different departments, ultimately leading to more informed decision-making.

Akira.ai offers organizations a suite of Agentic AI, which comprises tailored AI agents aimed at refining and automating intricate workflows across multiple sectors. These agents work alongside human teams to improve productivity, facilitate prompt decision-making, and handle monotonous tasks, including data analysis, HR operations, and incident management. The platform is designed to seamlessly integrate with current systems such as CRMs and ERPs, enabling a smooth shift to AI-driven processes without disruption. By implementing Akira’s AI agents, businesses can enhance their operational efficiency, accelerate decision-making, and foster innovation in industries such as finance, IT, and manufacturing. Ultimately, this collaboration between AI and human teams paves the way for significant advancements in productivity and operational excellence.

Wherobots provides a seamless way for users to create, test, and implement geospatial data analytics and AI pipelines directly within their current data ecosystem, with the option for cloud deployment. This solution alleviates concerns regarding resource management, scalability of workloads, and the complexities of geospatial processing and optimization. By linking your Wherobots account to the cloud database housing your data via our user-friendly SaaS web interface, you can efficiently build your geospatial data science, machine learning, or analytics applications using the Sedona Developer Tool. You can also automate the deployment of your geospatial pipeline to the cloud data platform while monitoring its performance through Wherobots. The results of your geospatial analytics tasks can be accessed in various ways, such as through a single geospatial map visualization or via API calls, ensuring flexibility in how insights are utilized. This comprehensive approach makes geospatial analytics more accessible and manageable for users at all levels of expertise.

Scalytics Connect combines data mesh and in-situ data processing with polystore technology, resulting in increased data scalability, increased data processing speed, and multiplying data analytics capabilities without losing privacy or security. You take advantage of all your data without wasting time with data copy or movement, enable innovation with enhanced data analytics, generative AI and federated learning (FL) developments. Scalytics Connect enables any organization to directly apply data analytics, train machine learning (ML) or generative AI (LLM) models on their installed data architecture.

Transform the way you view time in data analytics. With the ability to access your business data almost instantly, you can operate directly from your dashboard without the need to consult the IT team repeatedly. Introducing Indexima DataHub, a revolutionary environment that empowers both operational and functional users to obtain immediate access to their data. Through an innovative fusion of a specialized indexing engine and machine learning capabilities, Indexima enables organizations to streamline and accelerate their analytics processes. Designed for robustness and scalability, this solution allows companies to execute queries on vast amounts of data—potentially up to tens of billions of rows—in mere milliseconds. The Indexima platform facilitates instant analytics on all your data with just a single click. Additionally, thanks to Indexima's new ROI and TCO calculator, you can discover the return on investment for your data platform in just 30 seconds, taking into account infrastructure costs, project deployment duration, and data engineering expenses while enhancing your analytical capabilities. Experience the future of data analytics and unlock unprecedented efficiency in your operations.

You determine the cluster size, node specifications, and a range of services, while Yandex Data Proc effortlessly sets up and configures Spark, Hadoop clusters, and additional components. Collaboration is enhanced through the use of Zeppelin notebooks and various web applications via a user interface proxy. You maintain complete control over your cluster with root access for every virtual machine. Moreover, you can install your own software and libraries on active clusters without needing to restart them. Yandex Data Proc employs instance groups to automatically adjust computing resources of compute subclusters in response to CPU usage metrics. Additionally, Data Proc facilitates the creation of managed Hive clusters, which helps minimize the risk of failures and data loss due to metadata issues. This service streamlines the process of constructing ETL pipelines and developing models, as well as managing other iterative operations. Furthermore, the Data Proc operator is natively integrated into Apache Airflow, allowing for seamless orchestration of data workflows. This means that users can leverage the full potential of their data processing capabilities with minimal overhead and maximum efficiency.

Impala offers rapid response times and accommodates numerous concurrent users for business intelligence and analytical inquiries within the Hadoop ecosystem, supporting technologies such as Iceberg, various open data formats, and multiple cloud storage solutions. Additionally, it exhibits linear scalability, even when deployed in environments with multiple tenants. The platform seamlessly integrates with Hadoop's native security measures and employs Kerberos for user authentication, while the Ranger module provides a means to manage permissions, ensuring that only authorized users and applications can access specific data. You can leverage the same file formats, data types, metadata, and frameworks for security and resource management as those used in your Hadoop setup, avoiding unnecessary infrastructure and preventing data duplication or conversion. For users familiar with Apache Hive, Impala is compatible with the same metadata and ODBC driver, streamlining the transition. It also supports SQL, which eliminates the need to develop a new implementation from scratch. With Impala, a greater number of users can access and analyze a wider array of data through a unified repository, relying on metadata that tracks information right from the source to analysis. This unified approach enhances efficiency and optimizes data accessibility across various applications.

Apache Phoenix provides low-latency OLTP and operational analytics on Hadoop by merging the advantages of traditional SQL with the flexibility of NoSQL. It utilizes HBase as its underlying storage, offering full ACID transaction support alongside late-bound, schema-on-read capabilities. Fully compatible with other Hadoop ecosystem tools such as Spark, Hive, Pig, Flume, and MapReduce, it establishes itself as a reliable data platform for OLTP and operational analytics through well-defined, industry-standard APIs. When a SQL query is executed, Apache Phoenix converts it into a series of HBase scans, managing these scans to deliver standard JDBC result sets seamlessly. The framework's direct interaction with the HBase API, along with the implementation of coprocessors and custom filters, enables performance metrics that can reach milliseconds for simple queries and seconds for larger datasets containing tens of millions of rows. This efficiency positions Apache Phoenix as a formidable choice for businesses looking to enhance their data processing capabilities in a Big Data environment.

Break free from the limitations of application silos, budget overruns, and outdated skills by leveraging our advanced data and analytics platform to accelerate growth. This sophisticated platform is tailored for effective data management and in-depth analytics, facilitating seamless scaling across various technological environments. Our innovative architecture is designed to comprehend the flow and transformation of data throughout its entire lifecycle. This capability supports the creation of resilient enterprise AI applications that can withstand future challenges. With a highly modular and flexible design, our platform accommodates a diverse range of components, allowing for effortless integration. Its multi-tenant architecture is specifically crafted to promote scalability. Additionally, advanced data visualization tools simplify the analysis of intricate data structures, leading to improved enterprise AI application development within an intuitive, low-code predictive environment. Built on a unique hybrid multi-cloud framework utilizing open-source community software, our platform is highly adaptable, secure, and cost-effective, making it an ideal choice for organizations seeking efficiency and innovation. Furthermore, this platform not only empowers businesses to harness their data effectively but also enhances collaboration across teams, fostering a culture of data-driven decision-making.

Apache Trafodion serves as a webscale SQL-on-Hadoop solution that facilitates transactional or operational processes within the Apache Hadoop ecosystem. By leveraging the inherent scalability, elasticity, and flexibility of Hadoop, Trafodion enhances its capabilities to ensure transactional integrity, which opens the door for a new wave of big data applications to operate seamlessly on Hadoop. The platform supports the full ANSI SQL language, allowing for JDBC/ODBC connectivity suitable for both Linux and Windows clients. It provides distributed ACID transaction protection that spans multiple statements, tables, and rows, all while delivering performance enhancements specifically designed for OLTP workloads through both compile-time and run-time optimizations. Trafodion is also equipped with a parallel-aware query optimizer that efficiently handles large datasets, enabling developers to utilize their existing SQL knowledge and boost productivity. Furthermore, its distributed ACID transactions maintain data consistency across various rows and tables, making it interoperable with a wide range of existing tools and applications. This solution is neutral to both Hadoop and Linux distributions, providing a straightforward integration path into any existing Hadoop infrastructure. Thus, Apache Trafodion not only enhances the power of Hadoop but also simplifies the development process for users.

Embrace a groundbreaking age of analytics through the Alteryx AI Platform. Equip your organization with streamlined data preparation, analytics powered by artificial intelligence, and accessible machine learning, all while ensuring governance and security are built in. This marks the dawn of a new era for data-driven decision-making accessible to every user and team at all levels. Enhance your teams' capabilities with a straightforward, user-friendly interface that enables everyone to develop analytical solutions that boost productivity, efficiency, and profitability. Foster a robust analytics culture by utilizing a comprehensive cloud analytics platform that allows you to convert data into meaningful insights via self-service data preparation, machine learning, and AI-generated findings. Minimize risks and safeguard your data with cutting-edge security protocols and certifications. Additionally, seamlessly connect to your data and applications through open API standards, facilitating a more integrated and efficient analytical environment. By adopting these innovations, your organization can thrive in an increasingly data-centric world.