Best Data Management Software for Hadoop - Page 4

Combine master data management solutions with those found in SAS 9.4, where SAS MDM operates as a web-based interface accessible via the SAS Data Management Console. This system delivers a cohesive and precise representation of organizational data by consolidating information from multiple sources into a singular master record. Additionally, SAS® Data Remediation and SAS® Task Manager synergistically enhance SAS MDM's capabilities, as well as those of other SAS products, including SAS® Data Management and SAS® Data Quality. Through SAS Data Remediation, users can address and rectify issues arising from business rules in both batch jobs and real-time processes within SAS MDM. Meanwhile, SAS Task Manager serves as a supportive tool that integrates seamlessly with SAS Workflow technologies, allowing users to manage workflows initiated by other SAS applications with ease. By enabling the initiation, cessation, and transition of workflows uploaded to the SAS Workflow server, this ecosystem empowers organizations to maintain efficient data management practices. Overall, the integration of these technologies creates a robust framework for handling master data effectively.

Complexity is the enemy of security. Simplify and scale fine-grained data access control. Dynamically authorize and audit every query to comply with data security and privacy regulations. Okera integrates seamlessly into your infrastructure – in the cloud, on premise, and with cloud-native and legacy tools. With Okera, data users can 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.

Secuvy, a next-generation cloud platform, automates data security, privacy compliance, and governance via AI-driven workflows. Unstructured data is treated with the best data intelligence. Secuvy, a next-generation cloud platform that automates data security, privacy compliance, and governance via AI-driven workflows is called Secuvy. Unstructured data is treated with the best data intelligence. Automated data discovery, customizable subjects access requests, user validations and data maps & workflows to comply with privacy regulations such as the ccpa or gdpr. Data intelligence is used to locate sensitive and private information in multiple data stores, both in motion and at rest. Our mission is to assist organizations in protecting their brand, automating processes, and improving customer trust in a world that is rapidly changing. We want to reduce human effort, costs and errors in handling sensitive data.

lakeFS allows you to control your data lake similarly to how you manage your source code, facilitating parallel pipelines for experimentation as well as continuous integration and deployment for your data. This platform streamlines the workflows of engineers, data scientists, and analysts who are driving innovation through data. As an open-source solution, lakeFS enhances the resilience and manageability of object-storage-based data lakes. With lakeFS, you can execute reliable, atomic, and versioned operations on your data lake, encompassing everything from intricate ETL processes to advanced data science and analytics tasks. It is compatible with major cloud storage options, including AWS S3, Azure Blob Storage, and Google Cloud Storage (GCS). Furthermore, lakeFS seamlessly integrates with a variety of modern data frameworks such as Spark, Hive, AWS Athena, and Presto, thanks to its API compatibility with S3. The platform features a Git-like model for branching and committing that can efficiently scale to handle exabytes of data while leveraging the storage capabilities of S3, GCS, or Azure Blob. In addition, lakeFS empowers teams to collaborate more effectively by allowing multiple users to work on the same dataset without conflicts, making it an invaluable tool for data-driven organizations.

Foghub streamlines the integration of IT and OT, enhancing data engineering and real-time intelligence at the edge. Its user-friendly, cross-platform design employs an open architecture to efficiently manage industrial time-series data. By facilitating the critical link between operational components like sensors, devices, and systems, and business elements such as personnel, processes, and applications, Foghub enables seamless automated data collection and engineering processes, including transformations, advanced analytics, and machine learning. The platform adeptly manages a diverse range of industrial data types, accommodating significant variety, volume, and velocity, while supporting a wide array of industrial network protocols, OT systems, and databases. Users can effortlessly automate data gathering related to production runs, batches, parts, cycle times, process parameters, asset health, utilities, consumables, and operator performance. Built with scalability in mind, Foghub provides an extensive suite of features to efficiently process and analyze large amounts of data, ensuring that businesses can maintain optimal performance and decision-making capabilities. As industries evolve and data demands increase, Foghub remains a pivotal solution for achieving effective IT/OT convergence.

Brainwave is transforming how you evaluate user access! With an innovative user interface, enhanced predictive controls, and comprehensive risk-scoring features, you can now conduct in-depth access risk analyses. The Autonomous Identity solution allows your teams to operate more effectively with a user-friendly, industry-recognized tool that speeds up your identity management initiatives (IGA). This empowers organizations to assess and make informed decisions regarding access to shared files and folders. You can inventory, categorize, review access, and ensure compliance irrespective of the environment, whether it be file servers, NAS, Sharepoint, Office 365, and beyond. Our flagship offering, Brainwave Identity GRC, is packed with analytical tools that make the most of your access inventory. Enjoy constant visibility across all resources at any given moment. Furthermore, Brainwave’s extensive inventory serves as an entitlement catalog that spans across various infrastructure, business applications, and data access points, ensuring a comprehensive overview of user permissions. This holistic approach promotes better security and informed decision-making.

Apache Kylin™ is a distributed, open-source Analytical Data Warehouse designed for Big Data, aimed at delivering OLAP (Online Analytical Processing) capabilities in the modern big data landscape. By enhancing multi-dimensional cube technology and precalculation methods on platforms like Hadoop and Spark, Kylin maintains a consistent query performance, even as data volumes continue to expand. This innovation reduces query response times from several minutes to just milliseconds, effectively reintroducing online analytics into the realm of big data. Capable of processing over 10 billion rows in under a second, Kylin eliminates the delays previously associated with report generation, facilitating timely decision-making. It seamlessly integrates data stored on Hadoop with popular BI tools such as Tableau, PowerBI/Excel, MSTR, QlikSense, Hue, and SuperSet, significantly accelerating business intelligence operations on Hadoop. As a robust Analytical Data Warehouse, Kylin supports ANSI SQL queries on Hadoop/Spark and encompasses a wide array of ANSI SQL functions. Moreover, Kylin’s architecture allows it to handle thousands of simultaneous interactive queries with minimal resource usage, ensuring efficient analytics even under heavy loads. This efficiency positions Kylin as an essential tool for organizations seeking to leverage their data for strategic insights.

SOLIXCloud CDP provides a cloud-based data management solution tailored for contemporary data-centric businesses. Utilizing open-source and cloud-native technologies, it enables organizations to effectively handle and analyze their structured, semi-structured, and unstructured data, facilitating advanced analytics, regulatory compliance, infrastructure efficiency, and robust data security. Key components of this platform include Solix Connect for efficient data ingestion, Solix Data Governance, Solix Metadata Management, and Solix Search, collectively forming a holistic framework for managing cloud data. This framework supports the development and operation of data-driven applications, including SQL data warehouses, machine learning models, and artificial intelligence systems, while addressing the increasing complexities associated with data management regulations, data retention policies, and consumer privacy concerns. In this way, SOLIXCloud CDP empowers companies to navigate the evolving landscape of data management with confidence.

The volume of data continues to increase, yet not all data carries the same significance. Companies that embrace cloud data management can effectively lower their enterprise data management expenses while ensuring security, compliance, high performance, and straightforward accessibility. As time passes, the value of content diminishes; however, organizations can still generate revenue from older data using innovative SaaS-based solutions. SOLIXCloud provides all the necessary features to achieve an ideal equilibrium between managing both historical and current data. In addition to its robust compliance functionalities for structured, unstructured, and semi-structured data, SOLIXCloud presents a comprehensive managed service for all types of enterprise data. Furthermore, Solix's metadata management framework serves as a complete solution for analyzing all enterprise metadata and lineage from a single, centralized repository, supported by a comprehensive business glossary that enhances organizational efficiency. This holistic approach allows businesses to derive insights from their data, regardless of its age.

Utilizing graph analytics throughout the customer lifecycle can help uncover hidden risks and unveil unexpected opportunities. Conventional Master Data Management (MDM) solutions struggle to accommodate the vast amounts of distributed and diverse data generated from various applications and external sources. The traditional methods of probabilistic matching in MDM are ineffective when dealing with siloed data sources, leading to missed connections and a lack of context, ultimately resulting in poor decision-making and uncapitalized business value. An inadequate MDM solution can have widespread repercussions, negatively impacting both the customer experience and operational efficiency. When there's no immediate access to comprehensive payment patterns, trends, and risks, your team’s ability to make informed decisions swiftly is compromised, compliance expenses increase, and expanding coverage becomes a challenge. If your data remains unintegrated, it creates fragmented customer experiences across different channels, business sectors, and regions. Efforts to engage customers on a personal level often fail, as they rely on incomplete and frequently outdated information, highlighting the urgent need for a more cohesive approach to data management. This lack of a unified data strategy not only hampers customer satisfaction but also stifles business growth opportunities.

Witboost is an adaptable, high-speed, and effective data management solution designed to help businesses fully embrace a data-driven approach while cutting down on time-to-market, IT spending, and operational costs. The system consists of various modules, each serving as a functional building block that can operate independently to tackle specific challenges or be integrated to form a comprehensive data management framework tailored to your organization’s requirements. These individual modules enhance particular data engineering processes, allowing for a seamless combination that ensures swift implementation and significantly minimizes time-to-market and time-to-value, thereby lowering the overall cost of ownership of your data infrastructure. As urban environments evolve, smart cities increasingly rely on digital twins to forecast needs and mitigate potential issues, leveraging data from countless sources and managing increasingly intricate telematics systems. This approach not only facilitates better decision-making but also ensures that cities can adapt efficiently to ever-changing demands.

A Kudu cluster comprises tables that resemble those found in traditional relational (SQL) databases. These tables can range from a straightforward binary key and value structure to intricate designs featuring hundreds of strongly-typed attributes. Similar to SQL tables, each Kudu table is defined by a primary key, which consists of one or more columns; this could be a single unique user identifier or a composite key such as a (host, metric, timestamp) combination tailored for time-series data from machines. The primary key allows for quick reading, updating, or deletion of rows. The straightforward data model of Kudu facilitates the migration of legacy applications as well as the development of new ones, eliminating concerns about encoding data into binary formats or navigating through cumbersome JSON databases. Additionally, tables in Kudu are self-describing, enabling the use of standard analysis tools like SQL engines or Spark. With user-friendly APIs, Kudu ensures that developers can easily integrate and manipulate their data. This approach not only streamlines data management but also enhances overall efficiency in data processing tasks.

Parquet was developed to provide the benefits of efficient, compressed columnar data representation to all projects within the Hadoop ecosystem. Designed with a focus on accommodating complex nested data structures, Parquet employs the record shredding and assembly technique outlined in the Dremel paper, which we consider to be a more effective strategy than merely flattening nested namespaces. This format supports highly efficient compression and encoding methods, and various projects have shown the significant performance improvements that arise from utilizing appropriate compression and encoding strategies for their datasets. Furthermore, Parquet enables the specification of compression schemes at the column level, ensuring its adaptability for future developments in encoding technologies. It is crafted to be accessible for any user, as the Hadoop ecosystem comprises a diverse range of data processing frameworks, and we aim to remain neutral in our support for these different initiatives. Ultimately, our goal is to empower users with a flexible and robust tool that enhances their data management capabilities across various applications.

Hypertable provides a high-performance, scalable database solution that enhances the efficiency of your big data applications while minimizing hardware usage. This platform offers exceptional efficiency and outperforms its competitors, leading to significant cost reductions for users. Its robust and proven architecture supports numerous services at Google. Users can enjoy the advantages of open-source technology backed by a vibrant and active community. With a C++ implementation, Hypertable ensures optimal performance. Additionally, it offers around-the-clock support for critical big data operations. Clients benefit from direct access to the expertise of the core developers behind Hypertable. Specifically engineered to address scalability challenges that traditional relational database management systems struggle with, Hypertable leverages a design model pioneered by Google to effectively tackle scaling issues, making it superior to other NoSQL alternatives available today. Its innovative approach not only resolves current scalability needs but also anticipates future demands in data management.

Pinot is built to efficiently handle OLAP queries on static data with minimal latency. It incorporates various pluggable indexing methods, including Sorted Index, Bitmap Index, and Inverted Index. While it currently lacks support for joins, this limitation can be mitigated by utilizing Trino or PrestoDB for querying purposes. The system offers an SQL-like language that enables selection, aggregation, filtering, grouping, ordering, and distinct queries on datasets. It comprises both offline and real-time tables, with real-time tables being utilized to address segments lacking offline data. Additionally, users can tailor the anomaly detection process and notification mechanisms to accurately identify anomalies. This flexibility ensures that users can maintain data integrity and respond proactively to potential issues.

Hudi serves as a robust platform for constructing streaming data lakes equipped with incremental data pipelines, all while utilizing a self-managing database layer that is finely tuned for lake engines and conventional batch processing. It effectively keeps a timeline of every action taken on the table at various moments, enabling immediate views of the data while also facilitating the efficient retrieval of records in the order they were received. Each Hudi instant is composed of several essential components, allowing for streamlined operations. The platform excels in performing efficient upserts by consistently linking a specific hoodie key to a corresponding file ID through an indexing system. This relationship between record key and file group or file ID remains constant once the initial version of a record is written to a file, ensuring stability in data management. Consequently, the designated file group encompasses all iterations of a collection of records, allowing for seamless data versioning and retrieval. This design enhances both the reliability and efficiency of data operations within the Hudi ecosystem.

Utilize widely-used open-source frameworks like Apache Hadoop, Spark, Hive, and Kafka with Azure HDInsight, a customizable and enterprise-level service designed for open-source analytics. Effortlessly manage vast data sets while leveraging the extensive open-source project ecosystem alongside Azure’s global capabilities. Transitioning your big data workloads to the cloud is straightforward and efficient. You can swiftly deploy open-source projects and clusters without the hassle of hardware installation or infrastructure management. The big data clusters are designed to minimize expenses through features like autoscaling and pricing tiers that let you pay solely for your actual usage. With industry-leading security and compliance validated by over 30 certifications, your data is well protected. Additionally, Azure HDInsight ensures you remain current with the optimized components tailored for technologies such as Hadoop and Spark, providing an efficient and reliable solution for your analytics needs. This service not only streamlines processes but also enhances collaboration across teams.

Harness the capabilities of both private and public clouds through a unique hybrid data platform tailored for contemporary data architectures, enabling data access from any location. Cloudera stands out as a hybrid data platform that offers unparalleled flexibility, allowing users to choose any cloud, any analytics solution, and any type of data. It streamlines data management and analytics, ensuring optimal performance, scalability, and security for data accessibility from anywhere. By leveraging Cloudera, organizations can benefit from the strengths of both private and public clouds, leading to quicker value realization and enhanced control over IT resources. Moreover, Cloudera empowers users to securely transfer data, applications, and individuals in both directions between their data center and various cloud environments, irrespective of the data's physical location. This bi-directional capability not only enhances operational efficiency but also fosters a more adaptable and responsive data strategy.

At Datametica, our innovative solutions significantly reduce risks and alleviate costs, time, frustration, and anxiety throughout the data warehouse migration process to the cloud. We facilitate the transition of your current data warehouse, data lake, ETL, and enterprise business intelligence systems to your preferred cloud environment through our automated product suite. Our approach involves crafting a comprehensive migration strategy that includes workload discovery, assessment, planning, and cloud optimization. With our Eagle tool, we provide insights from the initial discovery and assessment phases of your existing data warehouse to the development of a tailored migration strategy, detailing what data needs to be moved, the optimal sequence for migration, and the anticipated timelines and expenses. This thorough overview of workloads and planning not only minimizes migration risks but also ensures that business operations remain unaffected during the transition. Furthermore, our commitment to a seamless migration process helps organizations embrace cloud technologies with confidence and clarity.

Doolytic is at the forefront of big data discovery, integrating data exploration, advanced analytics, and the vast potential of big data. The company is empowering skilled BI users to participate in a transformative movement toward self-service big data exploration, uncovering the inherent data scientist within everyone. As an enterprise software solution, doolytic offers native discovery capabilities specifically designed for big data environments. Built on cutting-edge, scalable, open-source technologies, doolytic ensures lightning-fast performance, managing billions of records and petabytes of information seamlessly. It handles structured, unstructured, and real-time data from diverse sources, providing sophisticated query capabilities tailored for expert users while integrating with R for advanced analytics and predictive modeling. Users can effortlessly search, analyze, and visualize data from any format and source in real-time, thanks to the flexible architecture of Elastic. By harnessing the capabilities of Hadoop data lakes, doolytic eliminates latency and concurrency challenges, addressing common BI issues and facilitating big data discovery without cumbersome or inefficient alternatives. With doolytic, organizations can truly unlock the full potential of their data assets.

IBM InfoSphere® Optim™ Data Privacy offers a comprehensive suite of tools designed to effectively mask sensitive information in non-production settings like development, testing, quality assurance, or training. This singular solution employs various transformation methods to replace sensitive data with realistic, fully functional masked alternatives, ensuring the confidentiality of critical information. Techniques for masking include using substrings, arithmetic expressions, generating random or sequential numbers, manipulating dates, and concatenating data elements. The advanced masking capabilities maintain contextually appropriate formats that closely resemble the original data. Users can apply an array of masking techniques on demand to safeguard personally identifiable information and sensitive corporate data within applications, databases, and reports. By utilizing these data masking features, organizations can mitigate the risk of data misuse by obscuring, privatizing, and protecting personal information circulated in non-production environments, thereby enhancing data security and compliance. Ultimately, this solution empowers businesses to navigate privacy challenges while maintaining the integrity of their operational processes.

Invenis serves as a robust platform for data analysis and mining, enabling users to easily clean, aggregate, and analyze their data while scaling efforts to enhance decision-making processes. It offers capabilities such as data harmonization, preparation, cleansing, enrichment, and aggregation, alongside powerful predictive analytics, segmentation, and recommendation features. By connecting seamlessly to various data sources like MySQL, Oracle, Postgres SQL, and HDFS (Hadoop), Invenis facilitates comprehensive analysis of diverse file formats, including CSV and JSON. Users can generate predictions across all datasets without requiring coding skills or a specialized team of experts, as the platform intelligently selects the most suitable algorithms based on the specific data and use cases presented. Additionally, Invenis automates repetitive tasks and recurring analyses, allowing users to save valuable time and fully leverage the potential of their data. Collaboration is also enhanced, as teams can work together, not only among analysts but across various departments, streamlining decision-making processes and ensuring that information flows efficiently throughout the organization. This collaborative approach ultimately empowers businesses to make better-informed decisions based on timely and accurate data insights.

A framework for distributed data integration that streamlines essential functions of Big Data integration, including data ingestion, replication, organization, and lifecycle management, is designed for both streaming and batch data environments. It operates as a standalone application on a single machine and can also function in an embedded mode. Additionally, it is capable of executing as a MapReduce application across various Hadoop versions and offers compatibility with Azkaban for initiating MapReduce jobs. In standalone cluster mode, it features primary and worker nodes, providing high availability and the flexibility to run on bare metal systems. Furthermore, it can function as an elastic cluster in the public cloud, maintaining high availability in this setup. Currently, Gobblin serves as a versatile framework for creating various data integration applications, such as ingestion and replication. Each application is usually set up as an independent job and managed through a scheduler like Azkaban, allowing for organized execution and management of data workflows. This adaptability makes Gobblin an appealing choice for organizations looking to enhance their data integration processes.

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Azkaban serves as a distributed Workflow Manager developed by LinkedIn to address the complexities of Hadoop job dependencies. There were instances where jobs required a specific order of execution, ranging from ETL processes to data analysis applications. Following the release of version 3.0, Azkaban offers two distinct operational modes: the standalone “solo-server” mode and the distributed multiple-executor mode. The solo-server mode utilizes an embedded H2 database, allowing both the web server and executor server to operate within the same process, making it ideal for initial experimentation or small-scale applications. In contrast, the multiple-executor mode is designed for serious production environments, requiring a MySQL database configured with a master-slave arrangement. Ideally, the web server and executor servers are hosted on separate machines to ensure that system upgrades and maintenance do not disrupt user experience. This configuration not only enhances Azkaban’s robustness but also significantly improves its scalability, making it suitable for larger, more complex workflows. By offering these two modes, Azkaban caters to a wide range of user needs, from casual experimentation to enterprise-level deployments.