Alternatives to WANdisco

Oracle Big Data Service simplifies the deployment of Hadoop clusters for customers, offering a range of VM configurations from 1 OCPU up to dedicated bare metal setups. Users can select between high-performance NVMe storage or more budget-friendly block storage options, and have the flexibility to adjust the size of their clusters as needed. They can swiftly establish Hadoop-based data lakes that either complement or enhance existing data warehouses, ensuring that all data is both easily accessible and efficiently managed. Additionally, the platform allows for querying, visualizing, and transforming data, enabling data scientists to develop machine learning models through an integrated notebook that supports R, Python, and SQL. Furthermore, this service provides the capability to transition customer-managed Hadoop clusters into a fully-managed cloud solution, which lowers management expenses and optimizes resource use, ultimately streamlining operations for organizations of all sizes. By doing so, businesses can focus more on deriving insights from their data rather than on the complexities of cluster management.

Apache Ranger™ serves as a framework designed to facilitate, oversee, and manage extensive data security within the Hadoop ecosystem. The goal of Ranger is to implement a thorough security solution throughout the Apache Hadoop landscape. With the introduction of Apache YARN, the Hadoop platform can effectively accommodate a genuine data lake architecture, allowing businesses to operate various workloads in a multi-tenant setting. As the need for data security in Hadoop evolves, it must adapt to cater to diverse use cases regarding data access, while also offering a centralized framework for the administration of security policies and the oversight of user access. This centralized security management allows for the execution of all security-related tasks via a unified user interface or through REST APIs. Additionally, Ranger provides fine-grained authorization, enabling specific actions or operations with any Hadoop component or tool managed through a central administration tool. It standardizes authorization methods across all Hadoop components and enhances support for various authorization strategies, including role-based access control, thereby ensuring a robust security framework. By doing so, it significantly strengthens the overall security posture of organizations leveraging Hadoop technologies.

K2View believes that every enterprise should be able to leverage its data to become as disruptive and agile as possible. We enable this through our Data Product Platform, which creates and manages a trusted dataset for every business entity – on demand, in real time. The dataset is always in sync with its sources, adapts to changes on the fly, and is instantly accessible to any authorized data consumer. We fuel operational use cases, including customer 360, data masking, test data management, data migration, and legacy application modernization – to deliver business outcomes at half the time and cost of other alternatives.

Effortlessly load your data into or extract it from Hadoop and data lakes, ensuring it is primed for generating reports, visualizations, or conducting advanced analytics—all within the data lakes environment. This streamlined approach allows you to manage, transform, and access data stored in Hadoop or data lakes through a user-friendly web interface, minimizing the need for extensive training. Designed specifically for big data management on Hadoop and data lakes, this solution is not simply a rehash of existing IT tools. It allows for the grouping of multiple directives to execute either concurrently or sequentially, enhancing workflow efficiency. Additionally, you can schedule and automate these directives via the public API provided. The platform also promotes collaboration and security by enabling the sharing of directives. Furthermore, these directives can be invoked from SAS Data Integration Studio, bridging the gap between technical and non-technical users. It comes equipped with built-in directives for various tasks, including casing, gender and pattern analysis, field extraction, match-merge, and cluster-survive operations. For improved performance, profiling processes are executed in parallel on the Hadoop cluster, allowing for the seamless handling of large datasets. This comprehensive solution transforms the way you interact with data, making it more accessible and manageable than ever.

Oracle Big Data SQL Cloud Service empowers companies to swiftly analyze information across various platforms such as Apache Hadoop, NoSQL, and Oracle Database, all while utilizing their existing SQL expertise, security frameworks, and applications, achieving remarkable performance levels. This solution streamlines data science initiatives and facilitates the unlocking of data lakes, making the advantages of Big Data accessible to a wider audience of end users. It provides a centralized platform for users to catalog and secure data across Hadoop, NoSQL systems, and Oracle Database. With seamless integration of metadata, users can execute queries that combine data from Oracle Database with that from Hadoop and NoSQL databases. Additionally, the service includes utilities and conversion routines that automate the mapping of metadata stored in HCatalog or the Hive Metastore to Oracle Tables. Enhanced access parameters offer administrators the ability to customize column mapping and govern data access behaviors effectively. Furthermore, the capability to support multiple clusters allows a single Oracle Database to query various Hadoop clusters and NoSQL systems simultaneously, thereby enhancing data accessibility and analytics efficiency. This comprehensive approach ensures that organizations can maximize their data insights without compromising on performance or security.

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.

Adoki optimizes the movement of data across various platforms and systems, including data warehouses, databases, cloud services, Hadoop environments, and streaming applications, catering to both one-time and scheduled transfers. It intelligently adjusts to the demands of your IT infrastructure, ensuring that transfer or replication tasks occur during the most efficient times. By providing centralized oversight and management of data transfers, Adoki empowers organizations to manage their data operations with a leaner and more effective team, ultimately enhancing productivity and reducing overhead.

Oracle Big Data Discovery is an impressively visual and user-friendly tool that harnesses the capabilities of Hadoop to swiftly convert unrefined data into actionable business insights in just minutes, eliminating the necessity for mastering complicated software or depending solely on highly trained individuals. This product enables users to effortlessly locate pertinent data sets within Hadoop, investigate the data to grasp its potential quickly, enhance and refine data for improved quality, analyze the information for fresh insights, and disseminate findings back to Hadoop for enterprise-wide utilization. By implementing BDD as the hub of your data laboratory, your organization can create a cohesive environment that facilitates the exploration of all data sources in Hadoop and the development of projects and BDD applications. Unlike conventional analytics tools, BDD allows a broader range of individuals to engage with big data, significantly reducing the time spent on loading and updating data, thereby allowing a greater focus on the actual analysis of substantial data sets. This shift not only streamlines workflows but also empowers teams to derive insights more efficiently and collaboratively.

Apache Sentry™ serves as a robust system for implementing detailed role-based authorization for both data and metadata within a Hadoop cluster environment. Achieving Top-Level Apache project status after graduating from the Incubator in March 2016, Apache Sentry is recognized for its effectiveness in managing granular authorization. It empowers users and applications to have precise control over access privileges to data stored in Hadoop, ensuring that only authenticated entities can interact with sensitive information. Compatibility extends to a range of frameworks, including Apache Hive, Hive Metastore/HCatalog, Apache Solr, Impala, and HDFS, though its primary focus is on Hive table data. Designed as a flexible and pluggable authorization engine, Sentry allows for the creation of tailored authorization rules that assess and validate access requests for various Hadoop resources. Its modular architecture increases its adaptability, making it capable of supporting a diverse array of data models within the Hadoop ecosystem. This flexibility positions Sentry as a vital tool for organizations aiming to manage their data security effectively.

IBM Db2 Big SQL is a sophisticated hybrid SQL-on-Hadoop engine that facilitates secure and advanced data querying across a range of enterprise big data sources, such as Hadoop, object storage, and data warehouses. This enterprise-grade engine adheres to ANSI standards and provides massively parallel processing (MPP) capabilities, enhancing the efficiency of data queries. With Db2 Big SQL, users can execute a single database connection or query that spans diverse sources, including Hadoop HDFS, WebHDFS, relational databases, NoSQL databases, and object storage solutions. It offers numerous advantages, including low latency, high performance, robust data security, compatibility with SQL standards, and powerful federation features, enabling both ad hoc and complex queries. Currently, Db2 Big SQL is offered in two distinct variations: one that integrates seamlessly with Cloudera Data Platform and another as a cloud-native service on the IBM Cloud Pak® for Data platform. This versatility allows organizations to access and analyze data effectively, performing queries on both batch and real-time data across various sources, thus streamlining their data operations and decision-making processes. In essence, Db2 Big SQL provides a comprehensive solution for managing and querying extensive datasets in an increasingly complex data landscape.

Harness the potential of your data, no matter its location. To truly embrace a data-driven approach, it's essential to utilize the entire range of information within your organization, spanning applications, cloud environments, and various systems. The CONNX data integration solution empowers you to seamlessly access, virtualize, and transfer your data—regardless of its format or location—without altering your foundational systems. Ensure your vital information is positioned effectively to enhance service delivery to your organization, clients, partners, and suppliers. This solution enables you to connect and modernize legacy data sources, transforming them from traditional databases to expansive data environments like Hadoop®, AWS, and Azure®. You can also migrate older systems to the cloud for improved scalability, transitioning from MySQL to Microsoft® Azure® SQL Database, SQL Server® to Amazon REDSHIFT®, or OpenVMS® Rdb to Teradata®, ensuring your data remains agile and accessible across all platforms. By doing so, you can maximize the efficiency and effectiveness of your data utilization strategies.

To effectively utilize the ZetaAnalytics product, a compatible database appliance is essential for the Data Warehouse setup. Landmark has successfully validated the ZetaAnalytics software with several systems including Teradata, EMC Greenplum, and IBM Netezza; for the latest approved versions, refer to the ZetaAnalytics Release Notes. Prior to the installation and configuration of the ZetaAnalytics software, it is crucial to ensure that your Data Warehouse is fully operational and prepared for data drilling. As part of the installation, you will need to execute scripts designed to create the specific database components necessary for Zeta within the Data Warehouse, and this process will require database administrator (DBA) access. Additionally, the ZetaAnalytics product relies on Apache Hadoop for model scoring and real-time data streaming, so if an Apache Hadoop cluster isn't already set up in your environment, it must be installed before you proceed with the ZetaAnalytics installer. During the installation, you will be prompted to provide the name and port number for your Hadoop Name Server as well as the Map Reducer. It is crucial to follow these steps meticulously to ensure a successful deployment of the ZetaAnalytics product and its features.

Bigtop is a project under the Apache Foundation designed for Infrastructure Engineers and Data Scientists who need a thorough solution for packaging, testing, and configuring leading open source big data technologies. It encompasses a variety of components and projects, such as Hadoop, HBase, and Spark, among others. By packaging Hadoop RPMs and DEBs, Bigtop simplifies the management and maintenance of Hadoop clusters. Additionally, it offers an integrated smoke testing framework, complete with a collection of over 50 test files to ensure reliability. For those looking to deploy Hadoop from scratch, Bigtop provides vagrant recipes, raw images, and in-progress docker recipes. The framework is compatible with numerous Operating Systems, including Debian, Ubuntu, CentOS, Fedora, and openSUSE, among others. Moreover, Bigtop incorporates a comprehensive set of tools and a testing framework that evaluates various aspects, such as packaging, platform, and runtime, which are essential for both new deployments and upgrades of the entire data platform, rather than just isolated components. This makes Bigtop a vital resource for anyone aiming to streamline their big data infrastructure.

Regardless of the location of your data—whether in cloud environments, traditional systems, or data lakes such as Hadoop—SAS Data Management provides the tools necessary to access the information you require. You can establish data management protocols once and apply them repeatedly, allowing for a consistent and efficient approach to enhancing and unifying data without incurring extra expenses. IT professionals often find themselves managing responsibilities beyond their typical scope, but SAS Data Management empowers your business users to make data updates, adjust workflows, and conduct their own analyses, thereby allowing you to concentrate on other initiatives. Moreover, the inclusion of a comprehensive business glossary along with SAS and third-party metadata management and lineage visualization features ensures that all team members remain aligned. The integrated nature of SAS Data Management technology means you won't have to deal with a disjointed solution; rather, all components, ranging from data quality to data federation, operate within a unified architecture, providing seamless functionality. This cohesive system fosters collaboration and enhances overall productivity across your organization.

The Apache Hadoop software library serves as a framework for the distributed processing of extensive data sets across computer clusters, utilizing straightforward programming models. It is built to scale from individual servers to thousands of machines, each providing local computation and storage capabilities. Instead of depending on hardware for high availability, the library is engineered to identify and manage failures within the application layer, ensuring that a highly available service can run on a cluster of machines that may be susceptible to disruptions. Numerous companies and organizations leverage Hadoop for both research initiatives and production environments. Users are invited to join the Hadoop PoweredBy wiki page to showcase their usage. The latest version, Apache Hadoop 3.3.4, introduces several notable improvements compared to the earlier major release, hadoop-3.2, enhancing its overall performance and functionality. This continuous evolution of Hadoop reflects the growing need for efficient data processing solutions in today's data-driven landscape.

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.

Transition your data, applications, and workloads to the cloud seamlessly, ensuring zero downtime with Arcserve Live Migration, which is specifically crafted to facilitate your cloud transformation without causing any disruptions. This solution allows for the effortless relocation of your essential data and workloads to your chosen cloud destination while maintaining uninterrupted business operations. By streamlining the cutover process, it reduces complexity and provides a centralized console for managing the entire migration journey. Arcserve Live Migration makes the task of moving data, applications, and workloads straightforward and efficient. Its versatile architecture supports the migration of almost any data type or workload to various environments, including cloud, on-premises, or remote locations like edge computing, and is compatible with virtual, cloud, and physical systems alike. Furthermore, it automatically keeps files, databases, and applications synchronized between Windows and Linux systems and a secondary physical or virtual environment, whether located on-site, at a remote site, or in the cloud, ensuring consistent data integrity throughout the process. This comprehensive approach not only enhances operational efficiency but also provides peace of mind during critical migrations.

Atlas serves as a versatile and scalable suite of essential governance services, empowering organizations to efficiently comply with regulations within the Hadoop ecosystem while facilitating integration across the enterprise's data landscape. Apache Atlas offers comprehensive metadata management and governance tools that assist businesses in creating a detailed catalog of their data assets, effectively classifying and managing these assets, and fostering collaboration among data scientists, analysts, and governance teams. It comes equipped with pre-defined types for a variety of both Hadoop and non-Hadoop metadata, alongside the capability to establish new metadata types tailored to specific needs. These types can incorporate primitive attributes, complex attributes, and object references, and they can also inherit characteristics from other types. Entities, which are instances of these types, encapsulate the specifics of metadata objects and their interconnections. Additionally, REST APIs enable seamless interaction with types and instances, promoting easier integration and enhancing overall functionality. This robust framework not only streamlines governance processes but also supports a culture of data-driven collaboration across the organization.

Streamline Your Migration Process! Transition your on-premises Document Management Systems and file shares seamlessly to either iManage Cloud or keep them on-premises. Utilizing an industry-standard ETL design that incorporates the steps of 'Extract', 'Transform', and 'Load', Cloud Migrator presents an effective method for cloud migration. This solution allows for the consolidation of databases, facilitates metadata mapping, and enables direct content migration to iManage Cloud while also providing options for data cleanup on-premises. It supports migration from various sources including eDocs, iManage (on-premises), Windows File Shares, and numerous structured database systems. Experience unparalleled migration speed, constrained only by your specific hardware, service provider, and internet service provider. Consolidate databases through many-to-one or many-to-many approaches, remap fields throughout the migration, and organize documents from flat structures into designated workspaces and folders. Furthermore, you can clean and modify data before transferring it using staging tables. There’s also the flexibility to map, adjust, and migrate existing metadata fields utilizing the provider's REST API, ensuring a tailored migration experience. This all-in-one solution makes migrating and managing your data easier than ever before.

Oracle Enterprise Metadata Management (OEMM) serves as a robust platform for managing metadata. It is capable of harvesting and cataloging metadata from a wide array of sources, such as relational databases, Hadoop, ETL processes, business intelligence systems, and data modeling tools, among others. Beyond merely acting as a repository for metadata, OEMM facilitates interactive searching and browsing of the data, while also offering features like data lineage tracking, impact analysis, and both semantic definition and usage analysis for any asset in its catalog. With its sophisticated algorithms, OEMM integrates metadata from various providers, creating a comprehensive view of the data journey from its origin to its final report or back. The platform's compatibility extends to numerous metadata sources, including data modeling tools, databases, CASE tools, ETL engines, data warehouses, BI systems, and EAI environments, among many others. This versatility ensures that organizations can effectively manage and utilize their metadata across diverse environments.

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.

PeerSync™ Migration simplifies the complexities associated with data migration in mixed storage environments, primarily through its robust features such as API integration and a dynamic real-time data replication engine that has successfully been deployed in numerous customer scenarios. Additionally, PeerFSA serves as a nimble tool that delivers insightful, comprehensive information about the organization, structure, and utilization of file data within intricate environments, ultimately enhancing migration performance and efficiency. It can automatically generate migration jobs by importing source and target pairs, streamlining the process for users. The accompanying graphics showcase various use cases for PeerSync Migration, highlighting its key benefit of real-time integration with major storage platforms, which effectively removes the need for final scans during non-disruptive migrations. Furthermore, PeerSync Migration provides the flexibility needed for efficient transitions to the cloud or the consolidation of file servers within both on-premises and cloud data centers, ensuring that organizations can adapt to their evolving storage needs with ease. This adaptability makes PeerSync Migration an essential tool for businesses seeking to optimize their data management strategies.

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.

When you have the expertise of an enterprise partner combined with a scalable, easy-to-use data management suite, you can take back control of your data, access it from anywhere, ensure security and compliance, and unlock its power to grow your business. Why Use Sesame Software? Relational Junction builds, populates, and incrementally refreshes your data automatically. Enhance Data Quality - Convert data from multiple sources into a consistent format – leading to more accurate data, which provides the basis for solid decisions. Gain Insights - Automate the update of information into a central location, you can use your in-house BI tools to build useful reports to avoid costly mistakes. Fixed Price - Avoid high consumption costs with yearly fixed prices and multi-year discounts no matter your data volume.

IBM Spectrum Virtualize™ and IBM Spectrum Virtualize™ for Public Cloud enable seamless mirroring between on-premises and cloud data centers, as well as between different cloud data centers. You can effortlessly transfer data between on-premises facilities and public cloud environments or across various public cloud platforms. This solution provides a uniform approach to data management, ensuring consistency across on-premises storage and public cloud resources. By integrating with existing on-premises software, you can replicate or migrate data from a vast array of over 500 supported storage systems, allowing you to enhance your hybrid cloud capabilities without incurring significant new expenses. With a flexible monthly pricing model, you only pay for the storage capacity you utilize in the public cloud. Additionally, you can implement effective disaster recovery strategies that span both on-premises and public cloud data centers. Furthermore, the solution facilitates cloud-based DevOps by simplifying the replication of data from local sources, streamlining development and operational processes. This integrated approach not only enhances efficiency but also supports innovation in data management practices.

EMR serves as a comprehensive enterprise-grade big data platform, offering cluster, job, and data management functionalities that leverage various open-source technologies, including Hadoop, Spark, Kafka, Flink, and Storm. Alibaba Cloud Elastic MapReduce (EMR) is specifically designed for big data processing within the Alibaba Cloud ecosystem. Built on Alibaba Cloud's ECS instances, EMR integrates the capabilities of open-source Apache Hadoop and Apache Spark. This platform enables users to utilize components from the Hadoop and Spark ecosystems, such as Apache Hive, Apache Kafka, Flink, Druid, and TensorFlow, for effective data analysis and processing. Users can seamlessly process data stored across multiple Alibaba Cloud storage solutions, including Object Storage Service (OSS), Log Service (SLS), and Relational Database Service (RDS). EMR also simplifies cluster creation, allowing users to establish clusters rapidly without the hassle of hardware and software configuration. Additionally, all maintenance tasks can be managed efficiently through its user-friendly web interface, making it accessible for various users regardless of their technical expertise.

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.

Support is available for data migrations from nearly 20 different types of sources, covering both on-premises and cloud environments. A distributed computing framework guarantees efficient data transfer and optimal writing for designated data sources. With a user-friendly wizard-based development interface, you can create migration tasks without the need for intricate programming, allowing for rapid task development. You only incur costs for what you utilize and can avoid the need for investing in dedicated hardware and software resources. Additionally, cloud services for big data can serve as a replacement or backup for on-premises big data systems, facilitating the complete migration of extensive data volumes. The compatibility with relational databases, big data formats, files, NoSQL, and numerous other data sources broadens its applicability. The intuitive task management feature enhances usability right out of the box. Data transfer occurs seamlessly between services on HUAWEI CLOUD, promoting greater data mobility and accessibility across platforms. This comprehensive solution empowers organizations to manage their data migration processes with ease and efficiency.

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.

AWS DataSync is a secure online solution designed to automate and speed up the transfer of data from on-premises storage to AWS Storage services. This service streamlines migration planning while significantly lowering the costs associated with on-premises data transfer through its fully managed architecture that can effortlessly adapt to increasing data volumes. It enables users to transfer data between various systems, including Network File System (NFS) shares, Server Message Block (SMB) shares, Hadoop Distributed File Systems (HDFS), self-managed object storage, as well as multiple AWS services such as AWS Snowcone, Amazon Simple Storage Service (Amazon S3), Amazon Elastic File System (Amazon EFS), and several Amazon FSx file systems. Moreover, DataSync facilitates the movement of data not only between AWS and on-premises environments but also across different public clouds, simplifying processes for replication, archiving, and data sharing for applications. With its robust end-to-end security measures, including data encryption and integrity checks, DataSync ensures that data remains protected throughout the transfer process, allowing businesses to focus on their core operations without worrying about data security. This comprehensive solution is ideal for organizations looking to enhance their data management capabilities in the cloud.

Alibaba Cloud Data Integration serves as a robust platform for data synchronization that allows for both real-time and offline data transfers among a wide range of data sources, networks, and geographical locations. It effectively facilitates the synchronization of over 400 different pairs of data sources, encompassing RDS databases, semi-structured and unstructured storage (like audio, video, and images), NoSQL databases, as well as big data storage solutions. Additionally, the platform supports real-time data interactions between various data sources, including popular databases such as Oracle and MySQL, along with DataHub. Users can easily configure offline tasks by defining specific triggers down to the minute, which streamlines the process of setting up periodic incremental data extraction. Furthermore, Data Integration seamlessly collaborates with DataWorks data modeling to create a cohesive operations and maintenance workflow. Utilizing the computational power of Hadoop clusters, the platform facilitates the synchronization of HDFS data with MaxCompute, ensuring efficient data management across multiple environments. By providing such extensive capabilities, it empowers businesses to enhance their data handling processes considerably.

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.

A SQL query engine that operates without a predefined schema, designed for use with Hadoop, NoSQL databases, and cloud storage solutions. This innovative engine allows for flexible data retrieval and analysis across various storage types, adapting seamlessly to diverse data structures.

The AWS Application Migration Service (MGN) is a sophisticated, automated lift-and-shift solution aimed at streamlining, expediting, and lowering the expenses associated with migrating applications from on-premises systems, private clouds, or other public cloud environments to AWS. This service operates by continuously replicating source servers at the block level within an AWS account, ensuring that the original setup of applications, operating systems, and data remains intact. Once organizations decide to proceed with the migration, MGN seamlessly transforms these replicated servers into native AWS resources like Amazon EC2 instances, allowing for a swift transition with minimal downtime and eliminating the need for application modifications. Designed to facilitate large-scale migrations, it boasts high compatibility, enabling businesses to transfer a multitude of physical, virtual, or cloud-based servers without causing performance issues or extending migration timeframes. Furthermore, MGN significantly decreases the amount of manual input required by automating crucial processes such as replication, conversion, and deployment, which helps in reducing the likelihood of errors during the migration process. By leveraging this service, companies can focus on their core operations rather than the complexities of migration logistics.

Binary Tree Power365® Migration by Quest enables effortless migration of mailboxes, archives, and content for Office 365 tenant migrations, all securely hosted on Microsoft Azure, ensuring a seamless cloud transformation experience. In addition to standard mailbox migrations, Power365 Migration allows for the transfer of OneDrive, OneNote, and SharePoint content, as well as facilitating migrations from both on-premises and hosted Exchange systems. This tool prioritizes data integrity and user confidence throughout the entire migration process, making it a reliable choice for organizations. Additionally, Power365 Migration is a truly unlimited solution, meaning you won’t encounter any worries regarding data caps, archive restrictions, or limits on the number of passes, promoting a swift and comprehensive migration. With the ability to schedule processes and synchronization events at convenient times, Binary Tree Power365 Migration enhances the overall experience for end-users, ensuring minimal disruption to business operations during the transition to Office 365. This flexibility and reliability make it an ideal solution for businesses looking to streamline their migration efforts effectively.

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.

Lentiq offers a collaborative data lake as a service that empowers small teams to achieve significant results. It allows users to swiftly execute data science, machine learning, and data analysis within the cloud platform of their choice. With Lentiq, teams can seamlessly ingest data in real time, process and clean it, and share their findings effortlessly. This platform also facilitates the building, training, and internal sharing of models, enabling data teams to collaborate freely and innovate without limitations. Data lakes serve as versatile storage and processing environments, equipped with machine learning, ETL, and schema-on-read querying features, among others. If you’re delving into the realm of data science, a data lake is essential for your success. In today’s landscape, characterized by the Post-Hadoop era, large centralized data lakes have become outdated. Instead, Lentiq introduces data pools—interconnected mini-data lakes across multiple clouds—that work harmoniously to provide a secure, stable, and efficient environment for data science endeavors. This innovative approach enhances the overall agility and effectiveness of data-driven projects.

Quest On Demand Migration is a cloud-focused platform aimed at making the transfer of workloads—such as email, files, and user data—easier by facilitating their movement to the cloud. This solution aids organizations in shifting from local systems or different cloud platforms to Microsoft 365, guaranteeing a smooth and secure migration experience. By incorporating automated migration features, it significantly cuts down on the manual workload and reduces downtime throughout the migration phase. Additionally, Quest On Demand Migration provides sophisticated tools for overseeing and tracking migration activities, along with real-time monitoring to ensure everything progresses without issues. It accommodates a variety of migration scenarios, including tenant-to-tenant migrations within Office 365, hybrid setups, and multi-cloud transitions. Furthermore, the platform includes comprehensive reporting and analytics capabilities, enabling administrators to keep tabs on the migration's status and swiftly address any complications that may arise. In addition, Quest On Demand Migration supports user and group management, making it an all-encompassing solution during the transition process, thereby enhancing overall operational efficiency.

The process of cloud migration raises numerous inquiries. Migrate for Compute Engine, a solution by Google Cloud, addresses these concerns effectively. Whether you aim to transfer a single application from your local servers or a thousand high-capacity applications across various data centers, Migrate for Compute Engine empowers IT teams of any size to shift their workloads seamlessly to Google Cloud. Its straightforward “as a service” interface within the Cloud Console, combined with adaptable migration options, simplifies the process, enabling users to significantly reduce the time and effort usually associated with migrations. Say goodbye to complicated setups, intricate configurations, and the confusion of client-side migration tools. By choosing the appropriate migration solution, your team can focus their energy on what truly counts: the successful transfer of workloads to the cloud. Ultimately, this tool not only streamlines the migration process but also enhances overall productivity and efficiency for IT teams.

SoftNAS is a cloud-native and software-defined enterprise cloud NAS filer product line. It can be used for primary data storage, secondary data storage, and hybrid cloud data integration. It allows existing applications to securely connect to the cloud without reengineering. SoftNAS offers enterprise-class NAS features such as high-availability and deduplication, compression and thin-provisioning. It also supports LDAP integration and Active Directory integration. SoftNAS protects mission critical data, primary, hot data, backup/archive, and makes cloud data migration more efficient and reliable. SoftNAS offers the most comprehensive storage options in terms price vs performance and backend storage choice, available on-demand at petabyte-scale across the AWS Marketplaces and Azure Marketplaces as well as on-premises on VMware.

Apache Mahout is an advanced and adaptable machine learning library that excels in processing distributed datasets efficiently. It encompasses a wide array of algorithms suitable for tasks such as classification, clustering, recommendation, and pattern mining. By integrating seamlessly with the Apache Hadoop ecosystem, Mahout utilizes MapReduce and Spark to facilitate the handling of extensive datasets. This library functions as a distributed linear algebra framework, along with a mathematically expressive Scala domain-specific language, which empowers mathematicians, statisticians, and data scientists to swiftly develop their own algorithms. While Apache Spark is the preferred built-in distributed backend, Mahout also allows for integration with other distributed systems. Matrix computations play a crucial role across numerous scientific and engineering disciplines, especially in machine learning, computer vision, and data analysis. Thus, Apache Mahout is specifically engineered to support large-scale data processing by harnessing the capabilities of both Hadoop and Spark, making it an essential tool for modern data-driven applications.

The AWS Mainframe Modernization service offers a distinctive platform that facilitates the migration and modernization of your on-premises mainframe applications into a fully-managed, cloud-native runtime environment on AWS. By transitioning your applications to the cloud, you can eliminate the hardware and personnel expenses associated with conventional mainframes. This service enables you to systematically manage your entire migration process through a combination of infrastructure, software, and tools designed to refactor and convert legacy applications. It also allows for accelerated modernization and extensive regression testing in a scalable manner with its cloud-native capabilities. As a comprehensive suite of managed tools, AWS Mainframe Modernization provides the necessary infrastructure and software to modernize, migrate, test, and operate mainframe applications effectively. Embarking on your mainframe modernization journey becomes easier, leading to improved outcomes by leveraging specialized knowledge in the field. In addition, it simplifies project complexity and fosters better collaboration across different teams. Furthermore, you can automate the transformation of legacy programming language applications into agile Java-based services, utilizing AWS Blu Age and contemporary web frameworks, ensuring your applications are not only modernized but also optimized for the future. This service positions your organization to remain competitive and responsive in an ever-evolving technological landscape.

WhereScape is a tool that helps IT organizations of any size to use automation to build, deploy, manage, and maintain data infrastructure faster. WhereScape automation is trusted by more than 700 customers around the world to eliminate repetitive, time-consuming tasks such as hand-coding and other tedious aspects of data infrastructure projects. This allows data warehouses, vaults and lakes to be delivered in days or weeks, rather than months or years.

IBM Cloud® Mass Data Migration leverages storage devices that offer 120 TB of usable space to streamline the transition of data to the cloud, effectively addressing typical transfer issues such as elevated costs, lengthy transfer durations, and security worries—all within one comprehensive service. With a single IBM Cloud Mass Data Migration device, users can transfer up to 120 TB of data (configured with RAID-6) in merely days, contrasting sharply with the weeks or even months required by conventional data transfer techniques. Whether your needs involve migrating a few terabytes or scaling up to multiple petabytes, you can easily request either a single device or several to meet your specific requirements. The process of shifting large datasets is often fraught with expense and delays; however, utilizing an IBM Cloud Mass Data Migration device at your site costs just $50 per day. IBM provides a preconfigured device that you can connect to, load your data onto, and then return for seamless integration into IBM Cloud Object Storage. After offloading, you’ll have immediate access to your data in the cloud, while IBM ensures the device is securely wiped clean. This innovative solution not only enhances efficiency but also simplifies the often complex and cumbersome task of large-scale data migration.

SAP BW/4HANA is an integrated data warehouse solution that utilizes SAP HANA technology. Serving as the on-premise component of SAP’s Business Technology Platform, it facilitates the consolidation of enterprise data, ensuring a unified and agreed-upon view across the organization. By providing a single source for real-time insights, it simplifies processes and fosters innovation. Leveraging the capabilities of SAP HANA, this advanced data warehouse empowers businesses to unlock the full potential of their data, whether sourced from SAP applications, third-party systems, or diverse data formats like unstructured, geospatial, or Hadoop-based sources. Organizations can transform their data management practices to enhance efficiency and agility, enabling the deployment of live insights at scale, whether hosted on-premise or in the cloud. Additionally, it supports the digitization of all business sectors, while integrating seamlessly with SAP’s digital business platform solutions. This approach allows companies to drive substantial improvements in decision-making and operational efficiency.