Alternatives to Stackable

Accelerate your data journey with AnalyticsCreator. Automate the design, development, and deployment of modern data architectures, including dimensional models, data marts, and data vaults or a combination of modeling techniques. Seamlessly integrate with leading platforms like Microsoft Fabric, Power BI, Snowflake, Tableau, and Azure Synapse and more. Experience streamlined development with automated documentation, lineage tracking, and schema evolution. Our intelligent metadata engine empowers rapid prototyping and deployment of analytics and data solutions. Reduce time-consuming manual tasks, allowing you to focus on data-driven insights and business outcomes. AnalyticsCreator supports agile methodologies and modern data engineering workflows, including CI/CD. Let AnalyticsCreator handle the complexities of data modeling and transformation, enabling you to unlock the full potential of your data

IRI Voracity is an end-to-end software platform for fast, affordable, and ergonomic data lifecycle management. Voracity speeds, consolidates, and often combines the key activities of data discovery, integration, migration, governance, and analytics in a single pane of glass, built on Eclipse™. Through its revolutionary convergence of capability and its wide range of job design and runtime options, Voracity bends the multi-tool cost, difficulty, and risk curves away from megavendor ETL packages, disjointed Apache projects, and specialized software. Voracity uniquely delivers the ability to perform data: * profiling and classification * searching and risk-scoring * integration and federation * migration and replication * cleansing and enrichment * validation and unification * masking and encryption * reporting and wrangling * subsetting and testing Voracity runs on-premise, or in the cloud, on physical or virtual machines, and its runtimes can also be containerized or called from real-time applications or batch jobs.

Domo puts data to work for everyone so they can multiply their impact on the business. Underpinned by a secure data foundation, our cloud-native data experience platform makes data visible and actionable with user-friendly dashboards and apps. Domo helps companies optimize critical business processes at scale and in record time to spark bold curiosity that powers exponential business results.

Amazon Redshift is the preferred choice among customers for cloud data warehousing, outpacing all competitors in popularity. It supports analytical tasks for a diverse range of organizations, from Fortune 500 companies to emerging startups, facilitating their evolution into large-scale enterprises, as evidenced by Lyft's growth. No other data warehouse simplifies the process of extracting insights from extensive datasets as effectively as Redshift. Users can perform queries on vast amounts of structured and semi-structured data across their operational databases, data lakes, and the data warehouse using standard SQL queries. Moreover, Redshift allows for the seamless saving of query results back to S3 data lakes in open formats like Apache Parquet, enabling further analysis through various analytics services, including Amazon EMR, Amazon Athena, and Amazon SageMaker. Recognized as the fastest cloud data warehouse globally, Redshift continues to enhance its performance year after year. For workloads that demand high performance, the new RA3 instances provide up to three times the performance compared to any other cloud data warehouse available today, ensuring businesses can operate at peak efficiency. This combination of speed and user-friendly features makes Redshift a compelling choice for organizations of all sizes.

Grow is a full-stack, no-code business intelligence (BI), platform that empowers everyone within your organization to make data-driven decision. Any organization can connect to its data and discover insights by combining ETL, data warehouses, and visualization in one platform. Our unlimited-user license model allows everyone to access the answers they seek without having to wait for an analyst. Everyone can now make great decisions in real time to accelerate their growth. Plus: Unlimited Users - More than 100 Integrations - No SQL required (but still available for use) - BI Consultants Support - Simple ETL Dynamic Dashboards

Equalum offers a unique continuous data integration and streaming platform that seamlessly accommodates real-time, batch, and ETL scenarios within a single, cohesive interface that requires no coding at all. Transition to real-time capabilities with an intuitive, fully orchestrated drag-and-drop user interface designed for ease of use. Enjoy the benefits of swift deployment, powerful data transformations, and scalable streaming data pipelines, all achievable in just minutes. With a multi-modal and robust change data capture (CDC) system, it enables efficient real-time streaming and data replication across various sources. Its design is optimized for exceptional performance regardless of the data origin, providing the advantages of open-source big data frameworks without the usual complexities. By leveraging the scalability inherent in open-source data technologies like Apache Spark and Kafka, Equalum's platform engine significantly enhances the efficiency of both streaming and batch data operations. This cutting-edge infrastructure empowers organizations to handle larger data volumes while enhancing performance and reducing the impact on their systems, ultimately facilitating better decision-making and quicker insights. Embrace the future of data integration with a solution that not only meets current demands but also adapts to evolving data challenges.

Apache Druid is a distributed data storage solution that is open source. Its fundamental architecture merges concepts from data warehouses, time series databases, and search technologies to deliver a high-performance analytics database capable of handling a diverse array of applications. By integrating the essential features from these three types of systems, Druid optimizes its ingestion process, storage method, querying capabilities, and overall structure. Each column is stored and compressed separately, allowing the system to access only the relevant columns for a specific query, which enhances speed for scans, rankings, and groupings. Additionally, Druid constructs inverted indexes for string data to facilitate rapid searching and filtering. It also includes pre-built connectors for various platforms such as Apache Kafka, HDFS, and AWS S3, as well as stream processors and others. The system adeptly partitions data over time, making queries based on time significantly quicker than those in conventional databases. Users can easily scale resources by simply adding or removing servers, and Druid will manage the rebalancing automatically. Furthermore, its fault-tolerant design ensures resilience by effectively navigating around any server malfunctions that may occur. This combination of features makes Druid a robust choice for organizations seeking efficient and reliable real-time data analytics solutions.

Peliqan.io provides a data platform that is all-in-one for business teams, IT service providers, startups and scale-ups. No data engineer required. Connect to databases, data warehouses, and SaaS applications. In a spreadsheet interface, you can explore and combine data. Business users can combine multiple data sources, clean data, edit personal copies, and apply transformations. Power users can use SQL on anything, and developers can use Low-code to create interactive data apps, implement writing backs and apply machine intelligence.

Connect and centralize data. Transform your data landscape into a flexible powerhouse. Data Virtuality is a data integration platform that allows for instant data access, data centralization, and data governance. Logical Data Warehouse combines materialization and virtualization to provide the best performance. For high data quality, governance, and speed-to-market, create your single source data truth by adding a virtual layer to your existing data environment. Hosted on-premises or in the cloud. Data Virtuality offers three modules: Pipes Professional, Pipes Professional, or Logical Data Warehouse. You can cut down on development time up to 80% Access any data in seconds and automate data workflows with SQL. Rapid BI Prototyping allows for a significantly faster time to market. Data quality is essential for consistent, accurate, and complete data. Metadata repositories can be used to improve master data management.

BigLake serves as a storage engine that merges the functionalities of data warehouses and lakes, allowing BigQuery and open-source frameworks like Spark to efficiently access data while enforcing detailed access controls. It enhances query performance across various multi-cloud storage systems and supports open formats, including Apache Iceberg. Users can maintain a single version of data, ensuring consistent features across both data warehouses and lakes. With its capacity for fine-grained access management and comprehensive governance over distributed data, BigLake seamlessly integrates with open-source analytics tools and embraces open data formats. This solution empowers users to conduct analytics on distributed data, regardless of its storage location or method, while selecting the most suitable analytics tools, whether they be open-source or cloud-native, all based on a singular data copy. Additionally, it offers fine-grained access control for open-source engines such as Apache Spark, Presto, and Trino, along with formats like Parquet. As a result, users can execute high-performing queries on data lakes driven by BigQuery. Furthermore, BigLake collaborates with Dataplex, facilitating scalable management and logical organization of data assets. This integration not only enhances operational efficiency but also simplifies the complexities of data governance in large-scale environments.

BryteFlow creates remarkably efficient automated analytics environments that redefine data processing. By transforming Amazon S3 into a powerful analytics platform, it skillfully utilizes the AWS ecosystem to provide rapid data delivery. It works seamlessly alongside AWS Lake Formation and automates the Modern Data Architecture, enhancing both performance and productivity. Users can achieve full automation in data ingestion effortlessly through BryteFlow Ingest’s intuitive point-and-click interface, while BryteFlow XL Ingest is particularly effective for the initial ingestion of very large datasets, all without the need for any coding. Moreover, BryteFlow Blend allows users to integrate and transform data from diverse sources such as Oracle, SQL Server, Salesforce, and SAP, preparing it for advanced analytics and machine learning applications. With BryteFlow TruData, the reconciliation process between the source and destination data occurs continuously or at a user-defined frequency, ensuring data integrity. If any discrepancies or missing information arise, users receive timely alerts, enabling them to address issues swiftly, thus maintaining a smooth data flow. This comprehensive suite of tools ensures that businesses can operate with confidence in their data's accuracy and accessibility.

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.

Experience an enterprise-level architecture and a cloud data platform powered by Snowflake® that enables secure storage and utilization of your data in the cloud. With integrated ETL and an intuitive drag-and-drop visual workflow designer, you can easily connect, clean, and transform data from over 250 sources. Harness cutting-edge Search and AI technology to quickly generate insights and actionable analytics from your data within seconds. Utilize our advanced AI/ML environments to create, refine, and deploy your predictive analytics and forecasting models. Take your data capabilities further with our comprehensive AI/ML frameworks, allowing you to design, train, and implement AI models through the PurpleCube Data Science module. Additionally, construct engaging BI visualizations with PurpleCube Analytics, explore your data using natural language searches, and benefit from AI-driven insights and intelligent recommendations that reveal answers to questions you may not have considered. This holistic approach ensures that you are equipped to make data-driven decisions with confidence and clarity.

INGEST. PREPARE. DELIVER. ALL WITH A SINGLE TOOL. Build a data infrastructure capable of ingesting, transforming, modeling, and delivering clean, reliable data in the fastest, most efficient way possible - all within a single, low-code user interface. ALL THE DATA INTEGRATION CAPABILITIES YOU NEED IN A SINGLE SOLUTION. TimeXtender seamlessly overlays and accelerates your data infrastructure, which means you can build an end-to-end data solution in days, not months - no more costly delays or disruptions. Say goodbye to a pieced-together Frankenstack of disconnected tools and systems. Say hello to a holistic solution for data integration that's optimized for agility. Unlock the full potential of your data with TimeXtender. Our comprehensive solution enables organizations to build future-proof data infrastructure and streamline data workflows, empowering every member of your team.

Enterprise Enabler brings together disparate information from various sources and isolated data sets, providing a cohesive view within a unified platform; this includes data housed in the cloud, distributed across isolated databases, stored on instruments, located in Big Data repositories, or found within different spreadsheets and documents. By seamlessly integrating all your data, it empowers you to make timely and well-informed business choices. The system creates logical representations of data sourced from its original locations, enabling you to effectively reuse, configure, test, deploy, and monitor everything within a single cohesive environment. This allows for the analysis of your business data as events unfold, helping to optimize asset utilization, reduce costs, and enhance your business processes. Remarkably, our deployment timeline is typically 50-90% quicker, ensuring that your data sources are connected and operational in record time, allowing for real-time decision-making based on the most current information available. With this solution, organizations can enhance collaboration and efficiency, leading to improved overall performance and strategic advantage in the market.

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.

Fully compatible with Netezza, this solution offers a streamlined command-line upgrade option. It can be deployed on-premises, in the cloud, or through a hybrid model. The IBM® Netezza® Performance Server for IBM Cloud Pak® for Data serves as a sophisticated platform for data warehousing and analytics, catering to both on-premises and cloud environments. With significant improvements in in-database analytics functions, this next-generation Netezza empowers users to engage in data science and machine learning with datasets that can reach petabyte levels. It includes features for detecting failures and ensuring rapid recovery, making it robust for enterprise use. Users can upgrade existing systems using a single command-line interface. The platform allows for querying multiple systems as a cohesive unit. You can select the nearest data center or availability zone, specify the desired compute units and storage capacity, and initiate the setup seamlessly. Furthermore, the IBM® Netezza® Performance Server is accessible on IBM Cloud®, Amazon Web Services (AWS), and Microsoft Azure, and it can also be implemented on a private cloud, all powered by the capabilities of IBM Cloud Pak for Data System. This flexibility enables organizations to tailor the deployment to their specific needs and infrastructure.

GeoSpock revolutionizes data integration for a connected universe through its innovative GeoSpock DB, a cutting-edge space-time analytics database. This cloud-native solution is specifically designed for effective querying of real-world scenarios, enabling the combination of diverse Internet of Things (IoT) data sources to fully harness their potential, while also streamlining complexity and reducing expenses. With GeoSpock DB, users benefit from efficient data storage, seamless fusion, and quick programmatic access, allowing for the execution of ANSI SQL queries and the ability to link with analytics platforms through JDBC/ODBC connectors. Analysts can easily conduct evaluations and disseminate insights using familiar toolsets, with compatibility for popular business intelligence tools like Tableau™, Amazon QuickSight™, and Microsoft Power BI™, as well as support for data science and machine learning frameworks such as Python Notebooks and Apache Spark. Furthermore, the database can be effortlessly integrated with internal systems and web services, ensuring compatibility with open-source and visualization libraries, including Kepler and Cesium.js, thus expanding its versatility in various applications. This comprehensive approach empowers organizations to make data-driven decisions efficiently and effectively.

Data Flow Manager is an on-premise tool designed to deploy & promote Apache NiFi data flows within minutes - no need for NiFi UI & controller services. Run unlimited NiFi data flows with pay-per-node pricing—no cloud, no CPU limits. Automate everything from NiFi flow deployment to promotion and scheduling. Monitor performance, enforce RBAC, and log every action. DFM even helps you build better NiFi flows with an AI-powered NiFi data flow creation assistant. Backed by 24x7 expert NiFi support and 99.99% uptime, DFM delivers total control and security.

Leverage your data, regardless of its location, with an open and hybrid data lakehouse designed specifically for AI and analytics. Seamlessly integrate data from various sources and formats, all accessible through a unified entry point featuring a shared metadata layer. Enhance both cost efficiency and performance by aligning specific workloads with the most suitable query engines. Accelerate the discovery of generative AI insights with integrated natural-language semantic search, eliminating the need for SQL queries. Ensure that your AI applications are built on trusted data to enhance their relevance and accuracy. Maximize the potential of all your data, wherever it exists. Combining the rapidity of a data warehouse with the adaptability of a data lake, watsonx.data is engineered to facilitate the expansion of AI and analytics capabilities throughout your organization. Select the most appropriate engines tailored to your workloads to optimize your strategy. Enjoy the flexibility to manage expenses, performance, and features with access to an array of open engines, such as Presto, Presto C++, Spark Milvus, and many others, ensuring that your tools align perfectly with your data needs. This comprehensive approach allows for innovative solutions that can drive your business forward.

Data stands as the most essential asset for any organization, but its true value emerges only when it is effectively utilized and optimized. As the volume of data continues to surge, and as enterprises grapple with disconnected application ecosystems and an increasing variety of data types, many find it challenging to maintain data quality. Through enterprise data management, organizations can extract valuable insights and discover new avenues for competitive advantage, which in turn aids in cost control, customer understanding, and enhancing profitability. OpenText's enterprise data management solutions cater to a wide array of needs, covering aspects such as data governance, quality, security, and beyond. By leveraging OpenText™ Alloy™, businesses can transcend mere data integration, transforming raw data into actionable insights. Alloy empowers organizations to harmonize, cleanse, enrich, and consolidate their data within a unified cloud platform, ultimately boosting automation and operational efficiency. This holistic approach not only streamlines data handling but also fosters informed decision-making, positioning organizations for long-term success.

A comprehensive and secure business data layer that delivers immediate insights for both business and data teams is essential. Stratio's generative AI data fabric encompasses the entire data management lifecycle, including data discovery, governance, utilization, and eventual disposal. In many organizations, data is scattered across various divisions, with different applications employed for distinct tasks. Stratio harnesses the power of AI to locate and access all your data, regardless of whether it resides on-premises or in the cloud. This ensures that your organization handles data in an appropriate manner. If you cannot visualize your data as soon as it is generated, you risk falling behind your customers' needs. Conventional data infrastructures often require hours to process customer data, hindering responsiveness. Stratio, however, enables real-time access to 100% of your data without necessitating its relocation, allowing you to respond swiftly while maintaining crucial context. Ultimately, by integrating operational and informational aspects within a collaborative platform, organizations can transition to leveraging instant extended AI capabilities for enhanced decision-making and agility. Embracing such a unified approach will empower businesses to thrive in a data-driven landscape.

VeloDB, which utilizes Apache Doris, represents a cutting-edge data warehouse designed for rapid analytics on large-scale real-time data. It features both push-based micro-batch and pull-based streaming data ingestion that occurs in mere seconds, alongside a storage engine capable of real-time upserts, appends, and pre-aggregations. The platform delivers exceptional performance for real-time data serving and allows for dynamic interactive ad-hoc queries. VeloDB accommodates not only structured data but also semi-structured formats, supporting both real-time analytics and batch processing capabilities. Moreover, it functions as a federated query engine, enabling seamless access to external data lakes and databases in addition to internal data. The system is designed for distribution, ensuring linear scalability. Users can deploy it on-premises or as a cloud service, allowing for adaptable resource allocation based on workload demands, whether through separation or integration of storage and compute resources. Leveraging the strengths of open-source Apache Doris, VeloDB supports the MySQL protocol and various functions, allowing for straightforward integration with a wide range of data tools, ensuring flexibility and compatibility across different environments.

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.

Materialize is an innovative reactive database designed to provide updates to views incrementally. It empowers developers to seamlessly work with streaming data through the use of standard SQL. One of the key advantages of Materialize is its ability to connect directly to a variety of external data sources without the need for pre-processing. Users can link to real-time streaming sources such as Kafka, Postgres databases, and change data capture (CDC), as well as access historical data from files or S3. The platform enables users to execute queries, perform joins, and transform various data sources using standard SQL, presenting the outcomes as incrementally-updated Materialized views. As new data is ingested, queries remain active and are continuously refreshed, allowing developers to create data visualizations or real-time applications with ease. Moreover, constructing applications that utilize streaming data becomes a straightforward task, often requiring just a few lines of SQL code, which significantly enhances productivity. With Materialize, developers can focus on building innovative solutions rather than getting bogged down in complex data management tasks.

Apache Doris serves as a cutting-edge data warehouse tailored for real-time analytics, enabling exceptionally rapid analysis of data at scale. It features both push-based micro-batch and pull-based streaming data ingestion that occurs within a second, alongside a storage engine capable of real-time upserts, appends, and pre-aggregation. With its columnar storage architecture, MPP design, cost-based query optimization, and vectorized execution engine, it is optimized for handling high-concurrency and high-throughput queries efficiently. Moreover, it allows for federated querying across various data lakes, including Hive, Iceberg, and Hudi, as well as relational databases such as MySQL and PostgreSQL. Doris supports complex data types like Array, Map, and JSON, and includes a Variant data type that facilitates automatic inference for JSON structures, along with advanced text search capabilities through NGram bloomfilters and inverted indexes. Its distributed architecture ensures linear scalability and incorporates workload isolation and tiered storage to enhance resource management. Additionally, it accommodates both shared-nothing clusters and the separation of storage from compute resources, providing flexibility in deployment and management.

Achieve limitless data retention for Apache Kafka® with Confluent, empowering you to be infrastructure-enabled rather than constrained by outdated systems. Traditional technologies often force a choice between real-time processing and scalability, but event streaming allows you to harness both advantages simultaneously, paving the way for innovation and success. Have you ever considered how your rideshare application effortlessly analyzes vast datasets from various sources to provide real-time estimated arrival times? Or how your credit card provider monitors millions of transactions worldwide, promptly alerting users to potential fraud? The key to these capabilities lies in event streaming. Transition to microservices and facilitate your hybrid approach with a reliable connection to the cloud. Eliminate silos to ensure compliance and enjoy continuous, real-time event delivery. The possibilities truly are limitless, and the potential for growth is unprecedented.

A data lakehouse represents a contemporary, open architecture designed for storing, comprehending, and analyzing comprehensive data sets. It merges the robust capabilities of traditional data warehouses with the extensive flexibility offered by widely used open-source data technologies available today. Constructing a data lakehouse can be accomplished on Oracle Cloud Infrastructure (OCI), allowing seamless integration with cutting-edge AI frameworks and pre-configured AI services such as Oracle’s language processing capabilities. With Data Flow, a serverless Spark service, users can concentrate on their Spark workloads without needing to manage underlying infrastructure. Many Oracle clients aim to develop sophisticated analytics powered by machine learning, applied to their Oracle SaaS data or other SaaS data sources. Furthermore, our user-friendly data integration connectors streamline the process of establishing a lakehouse, facilitating thorough analysis of all data in conjunction with your SaaS data and significantly accelerating the time to achieve solutions. This innovative approach not only optimizes data management but also enhances analytical capabilities for businesses looking to leverage their data effectively.

Oversee and protect the entire data lifecycle from the Edge to AI across any cloud platform or data center. Functions seamlessly within all leading public cloud services as well as private clouds, providing a uniform public cloud experience universally. Unifies data management and analytical processes throughout the data lifecycle, enabling access to data from any location. Ensures the implementation of security measures, regulatory compliance, migration strategies, and metadata management in every environment. With a focus on open source, adaptable integrations, and compatibility with various data storage and computing systems, it enhances the accessibility of self-service analytics. This enables users to engage in integrated, multifunctional analytics on well-managed and protected business data, while ensuring a consistent experience across on-premises, hybrid, and multi-cloud settings. Benefit from standardized data security, governance, lineage tracking, and control, all while delivering the robust and user-friendly cloud analytics solutions that business users need, effectively reducing the reliance on unauthorized IT solutions. Additionally, these capabilities foster a collaborative environment where data-driven decision-making is streamlined and more efficient.

Experience immediate advantages for your business right after installation. Discover the functionality of our system, which stands out as the quickest and most user-friendly solution for evaluating extensive geographically dispersed data. This innovative method of analytics breaks free from the limitations typically found in conventional big data warehouses, database designs, and edge computing frameworks. Gain insights into the platform's features that facilitate centralized management and control, streamline automated software setup and orchestration, and support data input and storage across diverse geographic locations. By adopting this new approach, you can enhance your data capabilities and drive growth more effectively than ever before.

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.

The Archon Data Store™ is a robust and secure platform built on open-source principles, tailored for archiving and managing extensive data lakes. Its compliance capabilities and small footprint facilitate large-scale data search, processing, and analysis across structured, unstructured, and semi-structured data within an organization. By merging the essential characteristics of both data warehouses and data lakes, Archon Data Store creates a seamless and efficient platform. This integration effectively breaks down data silos, enhancing data engineering, analytics, data science, and machine learning workflows. With its focus on centralized metadata, optimized storage solutions, and distributed computing, the Archon Data Store ensures the preservation of data integrity. Additionally, its cohesive strategies for data management, security, and governance empower organizations to operate more effectively and foster innovation at a quicker pace. By offering a singular platform for both archiving and analyzing all organizational data, Archon Data Store not only delivers significant operational efficiencies but also positions your organization for future growth and agility.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.

SQream is an advanced data analytics platform powered by GPU technology that allows companies to analyze large and intricate datasets with remarkable speed and efficiency. By utilizing NVIDIA's powerful GPU capabilities, SQream can perform complex SQL queries on extensive datasets in a fraction of the time, turning processes that traditionally take hours into mere minutes. The platform features dynamic scalability, enabling organizations to expand their data operations seamlessly as they grow, without interrupting ongoing analytics workflows. SQream's flexible architecture caters to a variety of deployment needs, ensuring it can adapt to different infrastructure requirements. Targeting sectors such as telecommunications, manufacturing, finance, advertising, and retail, SQream equips data teams with the tools to extract valuable insights, promote data accessibility, and inspire innovation, all while significantly cutting costs. This ability to enhance operational efficiency provides a competitive edge in today’s data-driven market.

The market experiences limited competition as a result of significant entry barriers, specialized expertise, substantial capital requirements, and extended time-to-market. Moreover, current platforms offer similar pricing and performance, which diminishes the motivation for users to transition. Transitioning from one SQL dialect to another can take months of intensive work. There is a demand for format-independent computing that can seamlessly work with all major open standards. Data leaders in enterprises are currently facing an extraordinary surge in the need for data intelligence. They are taken aback to discover that a mere 10% of their most demanding, compute-heavy tasks account for 80% of the costs, engineering resources, and stakeholder grievances. Regrettably, these workloads are also essential and cannot be neglected. e6data enhances the return on investment for a company's current data platforms and infrastructure. Notably, e6data’s format-agnostic computing stands out for its remarkable efficiency and performance across various leading data lakehouse table formats, thereby providing a significant advantage in optimizing enterprise operations. This innovative solution positions organizations to better manage their data-driven demands while maximizing their existing resources.

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 Camel is an open-source integration framework that simplifies the process of connecting diverse systems that either generate or consume data. It encompasses a wide range of Enterprise Integration Patterns, as outlined in the notable book by Gregor Hohpe and Bobby Woolf, and also incorporates contemporary integration patterns derived from microservice architectures to effectively address integration challenges using established best practices right out of the box. This framework can function independently or be integrated as a library within environments such as Spring Boot, Quarkus, application servers, or cloud platforms. The various subprojects within Camel are designed to enhance user experience and streamline tasks. With an extensive collection of several hundred components, Camel facilitates access to databases, message queues, APIs, and virtually any other resource imaginable. Moreover, it supports approximately 50 different data formats, enabling seamless message translation across multiple formats, with compatibility for industry-standard types from sectors like finance, telecommunications, and healthcare, thereby ensuring comprehensive integration capabilities. This versatility makes Camel a powerful tool for developers aiming to create robust integration solutions across various platforms and industries.

Databend is an innovative, cloud-native data warehouse crafted to provide high-performance and cost-effective analytics for extensive data processing needs. Its architecture is elastic, allowing it to scale dynamically in response to varying workload demands, thus promoting efficient resource use and reducing operational expenses. Developed in Rust, Databend delivers outstanding performance through features such as vectorized query execution and columnar storage, which significantly enhance data retrieval and processing efficiency. The cloud-first architecture facilitates smooth integration with various cloud platforms while prioritizing reliability, data consistency, and fault tolerance. As an open-source solution, Databend presents a versatile and accessible option for data teams aiming to manage big data analytics effectively in cloud environments. Additionally, its continuous updates and community support ensure that users can take advantage of the latest advancements in data processing technology.

Upsolver makes it easy to create a governed data lake, manage, integrate, and prepare streaming data for analysis. Only use auto-generated schema on-read SQL to create pipelines. A visual IDE that makes it easy to build pipelines. Add Upserts to data lake tables. Mix streaming and large-scale batch data. Automated schema evolution and reprocessing of previous state. Automated orchestration of pipelines (no Dags). Fully-managed execution at scale Strong consistency guarantee over object storage Nearly zero maintenance overhead for analytics-ready information. Integral hygiene for data lake tables, including columnar formats, partitioning and compaction, as well as vacuuming. Low cost, 100,000 events per second (billions every day) Continuous lock-free compaction to eliminate the "small file" problem. Parquet-based tables are ideal for quick queries.

DataNimbus, an AI-powered platform, streamlines payments and accelerates AI implementation through innovative solutions. DataNimbus improves scalability and governance by seamlessly integrating Databricks components such as Spark, Unity Catalog and ML Ops. Its offerings include a designer, a marketplace of reusable connectors and blocks for machine learning, and agile APIs. All are designed to simplify workflows while driving data-driven innovation.

Open core technology facilitates the integration of hybrid and multi-cloud environments. Built on the open-source initiative CDAP, Data Fusion guarantees portability of data pipelines for its users. The extensive compatibility of CDAP with both on-premises and public cloud services enables Cloud Data Fusion users to eliminate data silos and access previously unreachable insights. Additionally, its seamless integration with Google’s top-tier big data tools enhances the user experience. By leveraging Google Cloud, Data Fusion not only streamlines data security but also ensures that data is readily available for thorough analysis. Whether you are constructing a data lake utilizing Cloud Storage and Dataproc, transferring data into BigQuery for robust data warehousing, or transforming data for placement into a relational database like Cloud Spanner, the integration capabilities of Cloud Data Fusion promote swift and efficient development while allowing for rapid iteration. This comprehensive approach ultimately empowers businesses to derive greater value from their data assets.

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.

Connecting every data source with analytics services on a single AI-powered platform will transform how people access, manage, and act on data and insights. All your data. All your teams. All your teams in one place. Create an open, lake-centric hub to help data engineers connect data from various sources and curate it. This will eliminate sprawl and create custom views for all. Accelerate analysis through the development of AI models without moving data. This reduces the time needed by data scientists to deliver value. Microsoft Teams, Microsoft Excel, and Microsoft Teams are all great tools to help your team innovate faster. Connect people and data responsibly with an open, scalable solution. This solution gives data stewards more control, thanks to its built-in security, compliance, and governance.

A live data platform based on Change Data Capture (CDC) facilitates the replication of diverse databases, supports real-time data integration, and assists in constructing a real-time data warehouse. By implementing CDC to synchronize production line information stored in DB2 and Oracle with a modern database, TapData has empowered an AI-enhanced real-time dispatch application that streamlines the semiconductor manufacturing process. The availability of real-time data has enabled prompt decision-making within the RTD application, resulting in quicker turnaround times and increased production yield. As a major telecommunications provider, the client operates numerous regional systems designed to serve local users effectively. By consolidating and integrating data from multiple sources and locations into a single centralized data repository, the client successfully established an order center that aggregates orders from various applications. Additionally, TapData integrates inventory data from over 500 stores, delivering real-time insights into stock availability and customer preferences, thereby significantly boosting supply chain efficiency. This innovative approach not only enhances operational performance but also positions the client to respond swiftly to market demands.