Alternatives to Archon Data Store

BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises. Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.

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

Amazon Redshift is the preferred choice for cloud data warehousing among a vast array of customers, surpassing its competitors. It supports analytical tasks for a diverse range of businesses, from Fortune 500 giants to emerging startups, enabling their evolution into multi-billion dollar organizations, as seen with companies like Lyft. The platform excels in simplifying the process of extracting valuable insights from extensive data collections. Users can efficiently query enormous volumes of both structured and semi-structured data across their data warehouse, operational databases, and data lakes, all using standard SQL. Additionally, Redshift allows seamless saving of query results back to your S3 data lake in open formats such as Apache Parquet, facilitating further analysis with other analytics tools like Amazon EMR, Amazon Athena, and Amazon SageMaker. Recognized as the fastest cloud data warehouse globally, Redshift continues to enhance its speed and performance every year. For demanding workloads, the latest RA3 instances deliver performance that can be up to three times greater than any other cloud data warehouse currently available. This remarkable capability positions Redshift as a leading solution for organizations aiming to streamline their data processing and analytical efforts.

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.

DataLakeHouse.io Data Sync allows users to replicate and synchronize data from operational systems (on-premises and cloud-based SaaS), into destinations of their choice, primarily Cloud Data Warehouses. DLH.io is a tool for marketing teams, but also for any data team in any size organization. It enables business cases to build single source of truth data repositories such as dimensional warehouses, data vaults 2.0, and machine learning workloads. Use cases include technical and functional examples, including: ELT and ETL, Data Warehouses, Pipelines, Analytics, AI & Machine Learning and Data, Marketing and Sales, Retail and FinTech, Restaurants, Manufacturing, Public Sector and more. DataLakeHouse.io has a mission: to orchestrate the data of every organization, especially those who wish to become data-driven or continue their data-driven strategy journey. DataLakeHouse.io, aka DLH.io, allows hundreds of companies manage their cloud data warehousing solutions.

Composable is an enterprise-grade DataOps platform designed for business users who want to build data-driven products and create data intelligence solutions. It can be used to design data-driven products that leverage disparate data sources, live streams, and event data, regardless of their format or structure. Composable offers a user-friendly, intuitive dataflow visual editor, built-in services that facilitate data engineering, as well as a composable architecture which allows abstraction and integration of any analytical or software approach. It is the best integrated development environment for discovering, managing, transforming, and analysing enterprise data.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

A data lake serves as a centralized hub for managing large-scale data and artificial intelligence operations, enabling the storage of both structured and unstructured data without limits. At the heart of the cloud-native data lake framework lies Data Lake Formation (DLF), which simplifies the process of creating a cloud-native data lake. DLF offers seamless integration with various computing engines, facilitating centralized metadata management and robust enterprise-level access controls. This system efficiently gathers structured, semi-structured, and unstructured data, supporting extensive data storage capabilities. Its architecture distinguishes between computing and storage, allowing for on-demand resource planning at minimal costs. Consequently, this enhances data processing efficiency, ensuring responsiveness to the evolving demands of businesses. Additionally, DLF automatically identifies and aggregates metadata from different engines, effectively addressing the challenges posed by data silos while promoting an organized data environment. The capabilities provided by DLF ultimately empower organizations to leverage their data assets more effectively.

Mozart Data is the all-in-one modern data platform for consolidating, organizing, and analyzing your data. Set up a modern data stack in an hour, without any engineering. Start getting more out of your data and making data-driven decisions today.

Open, hybrid data lakes for AI and analytics can be used to put your data to use, wherever it is located. Connect your data in any format and from anywhere. Access it through a shared metadata layer. By matching the right workloads to the right query engines, you can optimize workloads in terms of price and performance. Integrate natural-language semantic searching without the need for SQL to unlock AI insights faster. Manage and prepare trusted datasets to improve the accuracy and relevance of your AI applications. Use all of your data everywhere. Watsonx.data offers the speed and flexibility of a warehouse, along with special features that support AI. This allows you to scale AI and analytics throughout your business. Choose the right engines to suit your workloads. You can manage your cost, performance and capability by choosing from a variety of open engines, including Presto C++ and Spark Milvus.

BigLake serves as a storage solution that merges data lakes and warehouses, allowing BigQuery and open-source frameworks like Spark to interact with data while maintaining detailed access controls. This engine boosts query efficiency across multi-cloud environments and supports open formats like Apache Iceberg. By storing a singular version of data with consistent features throughout both data lakes and warehouses, BigLake ensures fine-grained access management and governance across distributed data sources. It seamlessly connects with various open-source analytics tools and supports open data formats, providing analytics capabilities no matter the location or method of data storage. Users can select the most suitable analytics tools, whether open-source or cloud-native, while relying on a single data repository. Additionally, BigLake facilitates detailed access control across open-source engines, including Apache Spark, Presto, and Trino, as well as in formats like Parquet. It enhances query performance on data lakes using BigQuery and integrates with Dataplex, enabling scalable management and organized data structures. This comprehensive approach empowers organizations to maximize their data's potential and efficiently manage their analytics processes.

Our platform is the only one that offers an end-to–end platform for AI-powered innovation. It can handle everything from data cleansing and structuring to creating and deploying advanced data-science models to infusing advanced analytics algorithms, to infusing Recommendation AI, to deducing outcomes with simple-to-deduce visualization dashboards as well as Explainable AI to track how the outcomes were calculated. Our platform provides a seamless, holistic data science experience. FutureAnalytica offers key features such as a robust Data Lakehouse and an AI Studio. There is also a comprehensive AI Marketplace. You can also get support from a world-class team of data-science experts (on a case-by-case basis). FutureAnalytica will help you save time, effort, and money on your data-science and AI journey. Start discussions with the leadership and then a quick technology assessment within 1-3 days. In 10-18 days, you can create ready-to-integrate AI solutions with FA's fully-automated data science & AI platform.

Limited competition due to high barriers to entry, specialized knowledge, massive capital requirements, and long times to market. The price and performance of existing platforms are virtually identical, reducing the incentive for a switch. It takes months to migrate from one engine's SQL dialect into another engine's SQL. Interoperable with all major standards. Data leaders in enterprise are being hit by a massive surge in computing demand. They are surprised to discover that 10% of heavy, compute-intensive uses cases consume 80% the cost, engineering efforts and stakeholder complaints. Unfortunately, these workloads are mission-critical and nondiscretionary. e6data increases ROI for enterprises' existing data platforms. e6data’s format-neutral computing is unique in that it is equally efficient and performant for all leading data lakehouse formats.

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.

Qlik Compose for Data Warehouses offers a modern approach to data warehouse creation and operations by automating and optimising the process. Qlik Compose automates the design of the warehouse, generates ETL code and quickly applies updates, all while leveraging best practices. Qlik Compose for Data Warehouses reduces time, cost, and risk for BI projects whether they are on-premises, or in the cloud. Qlik Compose for Data Lakes automates data pipelines, resulting in analytics-ready data. By automating data ingestion and schema creation, as well as continual updates, organizations can realize a faster return on their existing data lakes investments.

The only fully-managed cloud data lakehouse that can ingest data from all of your sources in minutes, and support all of your query engines on a large scale. All for a fraction the cost. With the ease of fully managed pipelines, you can ingest data from databases and event streams in near-real-time. You can query your data using any engine and support all of your use cases, including BI, AI/ML, real-time analytics and AI/ML. Simple usage-based pricing allows you to cut your costs by up to 50% compared with cloud data warehouses and ETL software. With a fully-managed, highly optimized cloud service, you can deploy in minutes and without any engineering overhead. Unify all your data into a single source and eliminate the need for data to be copied between data lakes and warehouses. Apache Hudi, Apache Iceberg and Delta Lake all offer omnidirectional interoperability, allowing you to choose the best table format for your needs. Configure managed pipelines quickly for database CDC and stream ingestion.

Delta Lake is an open-source storage platform that allows ACID transactions to Apache Spark™, and other big data workloads. Data lakes often have multiple data pipelines that read and write data simultaneously. This makes it difficult for data engineers to ensure data integrity due to the absence of transactions. Your data lakes will benefit from ACID transactions with Delta Lake. It offers serializability, which is the highest level of isolation. Learn more at Diving into Delta Lake - Unpacking the Transaction log. Even metadata can be considered "big data" in big data. Delta Lake treats metadata the same as data and uses Spark's distributed processing power for all its metadata. Delta Lake is able to handle large tables with billions upon billions of files and partitions at a petabyte scale. Delta Lake allows developers to access snapshots of data, allowing them to revert to earlier versions for audits, rollbacks, or to reproduce experiments.

Discover insights to boost sales growth with code-free, fully automated data pipelines to data lakes and cloud warehouses. Flexible, standards-based platform that unifies sales and marketing data to automate insights and smarter growth. Say goodbye to manual data downloads that are expensive and messy. You will always know exactly what you'll be charged and only pay what you actually use. Access to data-ready data is a great way to fuel your tools. We only work with official APIs as certified developers. Data pipelines from well-known sources are easy to use. These data pipelines are pre-built, pre-transformed and ready to go. Unlock data from Amazon Vendor Central and Amazon Seller Central, Instagram Stories. Teams can quickly and economically realize the value of their data with code-free data ingestion and transformation. Databricks, Amazon Redshift and other trusted data destinations like Databricks or Amazon Redshift ensure that data is always protected.

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

BryteFlow creates the most efficient and automated environments for analytics. It transforms Amazon S3 into a powerful analytics platform by intelligently leveraging AWS ecosystem to deliver data at lightning speed. It works in conjunction with AWS Lake Formation and automates Modern Data Architecture, ensuring performance and productivity.

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.

The classic data lake is often reduced to simple but inexpensive raw data storage. This neglects important aspects like data quality, security, and transformation. These topics are left to data scientists who spend up to 80% of their time cleaning, understanding, and acquiring data before they can use their core competencies. Additionally, traditional Data Lakes are often implemented in different departments using different standards and tools. This makes it difficult to implement comprehensive analytical use cases. Smart Data Lakes address these issues by providing methodical and architectural guidelines as well as an efficient tool to create a strong, high-quality data foundation. Smart Data Lakes are the heart of any modern analytics platform. They integrate all the most popular Data Science tools and open-source technologies as well as AI/ML. Their storage is affordable and scalable, and can store both structured and unstructured data.

Data lakehouse is an open architecture that allows you to store, understand and analyze all of your data. It combines the power, richness, and flexibility of data warehouses with the breadth of open-source data technologies. A data lakehouse can easily be built on Oracle Cloud Infrastructure (OCI). It can also be used with pre-built AI services such as Oracle's language service and the latest AI frameworks. Data Flow, a serverless Spark service, allows our customers to concentrate on their Spark workloads using zero infrastructure concepts. Customers of Oracle want to build machine learning-based analytics on their Oracle SaaS data or any SaaS data. Our easy-to-use connectors for Oracle SaaS make it easy to create a lakehouse to analyze all of your SaaS data and reduce time to solve problems.

IBM Storage Scale, a software-defined object and file storage, allows organizations to build global data platforms for artificial intelligence (AI), advanced analytics and high-performance computing. Unlike traditional applications that work with structured data, today's performance-intensive AI and analytics workloads operate on unstructured data, such as documents, audio, images, videos, and other objects. IBM Storage Scale provides global data abstractions services that seamlessly connect data sources in multiple locations, even non-IBM storage environments. It is based on a massively-parallel file system that can be deployed across multiple hardware platforms, including x86 and IBM Power mainframes as well as ARM-based POSIX clients, virtual machines and Kubernetes.

Talend Data Fabric's cloud services are able to efficiently solve all your integration and integrity problems -- on-premises or in cloud, from any source, at any endpoint. Trusted data delivered at the right time for every user. With an intuitive interface and minimal coding, you can easily and quickly integrate data, files, applications, events, and APIs from any source to any location. Integrate quality into data management to ensure compliance with all regulations. This is possible through a collaborative, pervasive, and cohesive approach towards data governance. High quality, reliable data is essential to make informed decisions. It must be derived from real-time and batch processing, and enhanced with market-leading data enrichment and cleaning tools. Make your data more valuable by making it accessible internally and externally. Building APIs is easy with the extensive self-service capabilities. This will improve customer engagement.

Qubole is an open, secure, and simple Data Lake Platform that enables machine learning, streaming, or ad-hoc analysis. Our platform offers end-to-end services to reduce the time and effort needed to run Data pipelines and Streaming Analytics workloads on any cloud. Qubole is the only platform that offers more flexibility and openness for data workloads, while also lowering cloud data lake costs up to 50%. Qubole provides faster access to trusted, secure and reliable datasets of structured and unstructured data. This is useful for Machine Learning and Analytics. Users can efficiently perform ETL, analytics, or AI/ML workloads in an end-to-end fashion using best-of-breed engines, multiple formats and libraries, as well as languages that are adapted to data volume and variety, SLAs, and organizational policies.

Dataleyk is a secure, fully-managed cloud platform for SMBs. Our mission is to make Big Data analytics accessible and easy for everyone. Dataleyk is the missing piece to achieving your data-driven goals. Our platform makes it easy to create a stable, flexible, and reliable cloud data lake without any technical knowledge. All of your company data can be brought together, explored with SQL, and visualized with your favorite BI tool. Dataleyk will modernize your data warehouse. Our cloud-based data platform is capable of handling both structured and unstructured data. Data is an asset. Dataleyk, a cloud-based data platform, encrypts all data and offers data warehousing on-demand. Zero maintenance may not be an easy goal. It can be a catalyst for significant delivery improvements, and transformative results.

Snowflake's platform eliminates data silos and simplifies architectures, so organizations can get more value from their data. The platform is designed as a single, unified product with automations that reduce complexity and help ensure everything "just works." Learn more about Snowflake's AI Data Cloud at snowflake.com (NYSE: SNOW)

With your own data shop, create new revenue streams from the data you already have. Narrative focuses on the fundamental principles that make buying or selling data simpler, safer, and more strategic. You must ensure that the data you have access to meets your standards. It is important to know who and how the data was collected. Access new supply and demand easily for a more agile, accessible data strategy. You can control your entire data strategy with full end-to-end access to all inputs and outputs. Our platform automates the most labor-intensive and time-consuming aspects of data acquisition so that you can access new data sources in days instead of months. You'll only ever have to pay for what you need with filters, budget controls and automatic deduplication.

AWS Lake Formation streamlines the process of establishing a secure data lake in just a few days. A data lake serves as a centralized, curated, and secured storage repository that accommodates all types of data, whether in its raw state or refined for analysis. By utilizing a data lake, organizations can dismantle data silos and amalgamate various forms of analytics to derive insights that inform more effective business decisions. However, the current methods of setting up and managing data lakes are often labor-intensive, intricate, and time-consuming. This includes tasks such as importing data from various sources, overseeing data flows, configuring partitions, activating encryption while managing keys, establishing transformation jobs, and tracking their performance. Additionally, it involves restructuring data into a columnar format, removing duplicate entries, and aligning related records. After the data is successfully loaded into the data lake, it is essential to implement fine-grained access controls for datasets and maintain a thorough audit trail over time across numerous analytics and machine learning (ML) platforms. As organizations increasingly rely on data-driven decision-making, the efficiency and security provided by services like AWS Lake Formation become ever more crucial.

Many customers of Amazon Web Services (AWS), require data storage and analytics solutions that are more flexible and agile than traditional data management systems. Data lakes are a popular way to store and analyze data. They allow companies to manage multiple data types, from many sources, and store these data in a central repository. AWS Cloud offers many building blocks to enable customers to create a secure, flexible, cost-effective data lake. These services include AWS managed services that allow you to ingest, store and find structured and unstructured data. AWS offers the data solution to support customers in building data lakes. This is an automated reference implementation that deploys an efficient, cost-effective, high-availability data lake architecture on AWS Cloud. It also includes a user-friendly console for searching for and requesting data.

Kylo is an enterprise-ready open-source data lake management platform platform for self-service data ingestion and data preparation. It integrates metadata management, governance, security, and best practices based on Think Big's 150+ big-data implementation projects. Self-service data ingest that includes data validation, data cleansing, and automatic profiling. Visual sql and an interactive transformation through a simple user interface allow you to manage data. Search and explore data and metadata. View lineage and profile statistics. Monitor the health of feeds, services, and data lakes. Track SLAs and troubleshoot performance. To enable user self-service, create batch or streaming pipeline templates in Apache NiFi. While organizations can spend a lot of engineering effort to move data into Hadoop, they often struggle with data governance and data quality. Kylo simplifies data ingest and shifts it to data owners via a simple, guided UI.

Azure Data Lake encompasses all necessary features that enable developers, data scientists, and analysts to effortlessly store various types of data, regardless of size or format, while facilitating diverse processing and analytical tasks across different platforms and programming languages. By eliminating the challenges associated with data ingestion and storage, it significantly accelerates the initiation of batch, streaming, and interactive analytics. Additionally, Azure Data Lake is designed to work harmoniously with existing IT infrastructures regarding identity, management, and security, thus simplifying data governance and overall management. It also provides seamless integration with operational databases and data warehouses, allowing users to enhance their current data applications. Drawing from extensive experience with enterprise clients and managing some of the largest data processing and analytics workloads for major Microsoft services such as Office 365, Xbox Live, Azure, Windows, Bing, and Skype, Azure Data Lake effectively addresses a multitude of productivity and scalability obstacles that hinder optimal data utilization. Consequently, organizations can leverage this powerful platform to unlock the full potential of their data assets and drive better decision-making processes.

AtScale streamlines and enhances business intelligence, leading to quicker insights, improved decision-making, and greater returns on your cloud analytics investments. By removing tedious data engineering tasks such as data curation and delivery for analysis, it allows teams to focus on strategic initiatives. Centralizing business definitions ensures that KPI reporting remains consistent across various BI platforms. This solution not only speeds up the process of gaining insights from data but also manages cloud computing expenses more effectively. You can utilize existing data security protocols for analytics regardless of the data's location. With AtScale’s Insights workbooks and models, users can conduct multidimensional Cloud OLAP analyses on datasets from diverse sources without the need for preparation or engineering of data. Our intuitive dimensions and measures are designed to facilitate quick insight generation that directly informs business strategies, ensuring that teams make informed decisions efficiently. Overall, AtScale empowers organizations to maximize their data's potential while minimizing the complexity associated with traditional analytics processes.

We make BI and Big Data analytics easier and more efficient. Our goal is to empower business users, make BI specialists and always-busy business more independent when solving data-driven business problems. Querona is a solution for those who have ever been frustrated by a lack in data, slow or tedious report generation, or a long queue to their BI specialist. Querona has a built-in Big Data engine that can handle increasing data volumes. Repeatable queries can be stored and calculated in advance. Querona automatically suggests improvements to queries, making optimization easier. Querona empowers data scientists and business analysts by giving them self-service. They can quickly create and prototype data models, add data sources, optimize queries, and dig into raw data. It is possible to use less IT. Users can now access live data regardless of where it is stored. Querona can cache data if databases are too busy to query live.

Varada's adaptive and dynamic big data indexing solution allows you to balance cost and performance with zero data-ops. Varada's big data indexing technology is a smart acceleration layer for your data lake. It remains the single source and truth and runs in the customer's cloud environment (VPC). Varada allows data teams to democratize data. It allows them to operationalize the entire data lake and ensures interactive performance without the need for data to be moved, modelled, or manually optimized. Our ability to dynamically and automatically index relevant data at the source structure and granularity is our secret sauce. Varada allows any query to meet constantly changing performance and concurrency requirements of users and analytics API calls. It also keeps costs predictable and under control. The platform automatically determines which queries to speed up and which data to index. Varada adjusts the cluster elastically to meet demand and optimize performance and cost.

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.

Datuum is an AI-powered data integration tool that offers a unique solution for organizations looking to streamline their data integration process. With our pre-trained AI engine, Datuum simplifies customer data onboarding by allowing for automated integration from various sources without coding. This reduces data preparation time and helps establish resilient connectors, ultimately freeing up time for organizations to focus on generating insights and improving the customer experience. At Datuum, we have over 40 years of experience in data management and operations, and we've incorporated our expertise into the core of our product. Our platform is designed to address the critical challenges faced by data engineers and managers while being accessible and user-friendly for non-technical specialists. By reducing up to 80% of the time typically spent on data-related tasks, Datuum can help organizations optimize their data management processes and achieve more efficient outcomes.

Data Lake Governance Center (DGC) is a one-stop platform for managing data design, development and integration. It simplifies big data operations and builds intelligent knowledge libraries. A simple visual interface allows you to build an enterprise-class platform for data lake governance. Streamline your data lifecycle, use metrics and analytics, and ensure good corporate governance. Get real-time alerts and help to define and monitor data standards. To create data lakes faster, you can easily set up data models, data integrations, and cleaning rules to facilitate the discovery of reliable data sources. Maximize data's business value. DGC can be used to create end-to-end data operations solutions for smart government, smart taxation and smart campus. Gain new insights into sensitive data across your entire organization. DGC allows companies to define business categories, classifications, terms.

EntelliFusion by Teksouth is a fully managed, end to end solution. EntelliFusion's architecture is a one-stop solution for outfitting a company's data infrastructure. Instead of trying to put together multiple platforms for data prep, data warehouse and governance, and then deploying a lot of IT resources to make it all work, EntelliFusion's architecture offers a single platform. EntelliFusion unites data silos into a single platform that allows for cross-functional KPI's. This creates powerful insights and holistic solutions. EntelliFusion's "military born" technology has been able to withstand the rigorous demands of the USA's top echelon in military operations. It was scaled up across the DOD over twenty years. EntelliFusion is built using the most recent Microsoft technologies and frameworks, which allows it to continue being improved and innovated. EntelliFusion is data-agnostic and infinitely scalable. It guarantees accuracy and performance to encourage end-user tool adoption.

Break down data silos by utilizing a unified storage solution that enhances cost efficiency through tiered storage and effective policy management. Ensure data integrity with Azure Active Directory (Azure AD) authentication and role-based access control (RBAC), while bolstering data protection through robust security measures such as encryption at rest and advanced threat protection. This solution is designed with high security in mind, featuring adaptable protection strategies for data access, encryption, and network control. It serves as a comprehensive platform for data ingestion, processing, and visualization, compatible with prevalent analytics frameworks. Cost effectiveness is achieved by independently scaling storage and compute resources, employing lifecycle policy management, and implementing object-level tiering. With the extensive Azure global infrastructure, you can effortlessly meet varying capacity needs and manage data seamlessly. Furthermore, the system enables the execution of large-scale analytics queries with consistently high performance, ensuring that your data operations remain efficient and effective.

AI teams are faced with challenges that require new technologies. These technologies are built by us. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. AI and software development go hand in hand. Built with data scientists, ML experts, and data engineers at heart. Don't reinvent your wheel! Production is fast and cost-effective. All your data is stored by you. Your machines are used to train your models. Existing data lakes and data warehouses do not work with unstructured data like text, images, or videos. New technologies are required for AI teams. These technologies are built by us. Studio is an extension to BitBucket, GitLab, and GitHub. Register for the online SaaS version, or contact us to start an on-premise installation

VeloDB, powered by Apache Doris is a modern database for real-time analytics at scale. In seconds, micro-batch data can be ingested using a push-based system. Storage engine with upserts, appends and pre-aggregations in real-time. Unmatched performance in real-time data service and interactive ad hoc queries. Not only structured data, but also semi-structured. Not only real-time analytics, but also batch processing. Not only run queries against internal data, but also work as an federated query engine to access external databases and data lakes. Distributed design to support linear scalability. Resource usage can be adjusted flexibly to meet workload requirements, whether on-premise or cloud deployment, separation or integration. Apache Doris is fully compatible and built on this open source software. Support MySQL functions, protocol, and SQL to allow easy integration with other tools.

DQOps is a data quality monitoring platform for data teams that helps detect and address quality issues before they impact your business. Track data quality KPIs on data quality dashboards and reach a 100% data quality score. DQOps helps monitor data warehouses and data lakes on the most popular data platforms. DQOps offers a built-in list of predefined data quality checks verifying key data quality dimensions. The extensibility of the platform allows you to modify existing checks or add custom, business-specific checks as needed. The DQOps platform easily integrates with DevOps environments and allows data quality definitions to be stored in a source repository along with the data pipeline code.

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.

Qlik Data Integration platform automates the process for providing reliable, accurate and trusted data sets for business analysis. Data engineers are able to quickly add new sources to ensure success at all stages of the data lake pipeline, from real-time data intake, refinement, provisioning and governance. This is a simple and universal solution to continuously ingest enterprise data into popular data lake in real-time. This model-driven approach allows you to quickly design, build, and manage data lakes in the cloud or on-premises. To securely share all your derived data sets, create a smart enterprise-scale database catalog.

Agile Data Engine serves as a robust DataOps platform aimed at optimizing the entire process of developing, deploying, and managing cloud-based data warehouses. This innovative solution combines data modeling, transformation, continuous deployment, workflow orchestration, monitoring, and API connectivity into a unified SaaS offering. By employing a metadata-driven methodology, it automates the generation of SQL code and the execution of data loading workflows, which significantly boosts efficiency and responsiveness in data operations. The platform is compatible with a variety of cloud database solutions, such as Snowflake, Databricks SQL, Amazon Redshift, Microsoft Fabric (Warehouse), Azure Synapse SQL, Azure SQL Database, and Google BigQuery, thus providing users with substantial flexibility across different cloud environments. Additionally, its modular product framework and ready-to-use CI/CD pipelines ensure that data teams can integrate seamlessly and maintain continuous delivery, allowing them to quickly respond to evolving business needs. Moreover, Agile Data Engine offers valuable insights and performance metrics, equipping users with the necessary tools to monitor and optimize their data platform. This enables organizations to maintain a competitive edge in today’s fast-paced data landscape.

SplineCloud is a knowledge management platform that facilitates the discovery, formalization and exchange of structured, reusable knowledge. It was designed for science and engineering. It allows users to organize their data into structured repositories that are easily accessible and findable. The platform provides tools like an online plot digitizer to extract data from graphs, and an interactive curve-fitting tool that allows users define functional relationships within datasets by using smooth spline function. Users can reuse datasets and relationships in their models and calculation by accessing them directly via the SplineCloud API, or by utilizing client libraries open-source for Python and MATLAB. The platform enables the development of reusable engineering applications and analytical applications. It aims to reduce redundancy and improve decision-making by preserving expert knowledge and reducing redundant design processes.

Xtract Data Automation Suite (XDAS) is a comprehensive platform designed to streamline process automation for data-intensive workflows. It offers a vast library of over 300 pre-built micro solutions and AI agents, enabling businesses to design and orchestrate AI-driven workflows with no code environment, thereby enhancing operational efficiency and accelerating digital transformation. By leveraging these tools, XDAS helps businesses ensure compliance, reduce time to market, enhance data accuracy, and forecast market trends across various industries.