Alternatives to lakeFS

#1 Enterprise File Sharing, Sync, Backup & Remote Access. You have complete control over your data and can manage it. You can either self-host it on-premises, or on the cloud. Your own Dropbox-like file sharing, sync and storage solution. FileCloud is hosted by us on a top-notch infrastructure in the region you choose. No installation. We handle all technical details. FileCloud can be run on your infrastructure. You have full control over your data. You can self-host FileCloud on AWS and AWS GovCloud. AWS and Azure marketplaces offer pre-built FileCloud images. Supports local storage (Disk and Network Shares, CIFS/NFS, and cloud storage. Multiple storage endpoints can be connected. Supports AWS S3, Azure Blob and Wasabi as well as other S3 compatible storage systems. Both file gateway (Network share), and primary (managed storage) modes are supported.

The most accurate, complete, and timely data provides the best insight. Minitab Connect empowers data users across the enterprise with self service tools to transform diverse data into a network of data pipelines that feed analytics initiatives, foster collaboration and foster organizational-wide collaboration. Users can seamlessly combine and explore data from various sources, including databases, on-premise and cloud apps, unstructured data and spreadsheets. Automated workflows make data integration faster and provide powerful data preparation tools that allow for transformative insights. Data integration tools that are intuitive and flexible allow users to connect and blend data from multiple sources such as data warehouses, IoT devices and cloud storage.

Secure, highly scalable object storage that is both highly scalable and scalable for cloud-native workloads. Azure Blob Storage allows you to create data lakes for your analytics and storage to build powerful cloud and mobile apps. Tiered storage reduces costs and allows you to scale up for machine learning and high-performance computing workloads. Blob storage was designed from the ground up for developers of mobile, web and cloud-native applications. It supports the scale, security and availability requirements. It can be used as a foundation for serverless architectures like Azure Functions. Blob storage supports all the most popular development frameworks such as Java,.NET and Python. It is also the only cloud storage service that offers a premium SSD-based object storage tier to support interactive and low-latency scenarios.

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.

Cribl Search is a next-generation search in place technology that empowers users to explore, discover and analyze data previously impossible. This includes data stored behind APIs, but also data at its source. Search your Cribl Lake easily or sift data from major object stores such as AWS S3, Amazon Security Lake Azure Blob and Google Cloud Storage. You can also enrich your insights with live API endpoints of various SaaS providers. Cribl Search's power lies in its strategic approach. It forwards only the most critical data to your analysis systems, avoiding expensive storage. Cribl Search offers native support for platforms like Amazon Security Lake, AWS Blob, Azure Blob and Google Cloud Storage. This is a first of its kind ability to analyze data at the source. Cribl Search lets users search and analyze data from debug logs on the edge, to archived data stored in cold storage.

BigLake is a storage platform that unifies data warehouses, lakes and allows BigQuery and open-source frameworks such as Spark to access data with fine-grained control. BigLake offers accelerated query performance across multicloud storage and open formats like Apache Iceberg. You can store one copy of your data across all data warehouses and lakes. Multi-cloud governance and fine-grained access control for distributed data. Integration with open-source analytics tools, and open data formats is seamless. You can unlock analytics on distributed data no matter where it is stored. While choosing the best open-source or cloud-native analytics tools over a single copy, you can also access analytics on distributed data. Fine-grained access control for open source engines such as Apache Spark, Presto and Trino and open formats like Parquet. BigQuery supports performant queries on data lakes. Integrates with Dataplex for management at scale, including logical organization.

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 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.

You can automatically ingest your marketing data into any cloud file storage or data warehouse of your choice, such as BigQuery and Snowflake, Redshift and Azure SQL, AWS S3, AzureData Lake, Google Cloud Storage, and our fully managed ETL pipelines. Our hosted marketing data warehouse integrates all marketing data and provides ad insight, cross-channelattribution, content insights and competitor Insights. Our customer data platform enables a single view of the customer and their journey by allowing identity resolution across all data sources in real time. Electrik.AI, a cloud-based marketing software and full-service platform, is cloud-based. Electrik.AI's Google Analytics hit data extractor enriches the hit level data sent by the website or application to Google Analytics and periodically ships it to the desired destination database/data warehouse/file/data lake.

Azure Data Lake offers all the capabilities needed to make it easy to store and analyze data across all platforms and languages. It eliminates the complexity of ingesting, storing, and streaming data, making it easier to get up-and-running with interactive, batch, and streaming analytics. Azure Data Lake integrates with existing IT investments to simplify data management and governance. It can also seamlessly integrate with existing IT investments such as data warehouses and operational stores, allowing you to extend your current data applications. We have the experience of working with enterprise customers, running large-scale processing and analytics for Microsoft businesses such as Office 365, Microsoft Windows, Bing, Azure, Windows, Windows, and Microsoft Windows. Azure Data Lake solves many productivity and scaling issues that prevent you from maximizing the potential of your data.

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.

Lentiq is a data lake that allows small teams to do big tasks. You can quickly run machine learning, data science, and data analysis at scale in any cloud. Lentiq allows your teams to ingest data instantly and then clean, process, and share it. Lentiq allows you to create, train, and share models within your organization. Lentiq allows data teams to collaborate and invent with no restrictions. Data lakes are storage and process environments that provide ML, ETL and schema-on-read querying capabilities. Are you working on data science magic? A data lake is a must. The big, centralized data lake of the Post-Hadoop era is gone. Lentiq uses data pools, which are interconnected, multi-cloud mini-data lakes. They all work together to provide a stable, secure, and fast data science environment.

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.

A data lake is a central repository for big data and AI computing. It allows you to store both structured and unstructured data at any size. Data Lake Formation (DLF), is a key component in the cloud-native database lake framework. DLF is a simple way to create a cloud-native database lake. It integrates seamlessly with a variety compute engines. You can manage metadata in data lakes in an centralized manner and control enterprise class permissions. It can systematically collect structured, semi-structured and unstructured data, and supports massive data storage. This architecture separates storage and computing. This allows you to plan resources on demand and at low costs. This increases data processing efficiency to meet rapidly changing business needs. DLF can automatically detect and collect metadata from multiple engines. It can also manage the metadata in a central manner to resolve data silo problems.

You can easily develop and execute massively parallel data processing and transformation programs in U-SQL and R. You don't need to maintain any infrastructure and can process data on-demand, scale instantly, or pay per job. Azure Data Lake Analytics makes it easy to process large data jobs in seconds. There are no servers, virtual machines or clusters to manage or tune. You can instantly scale your processing power in Azure Data Lake Analytics Units, (AU), to one to thousands per job. Only pay for the processing you use per job. Optimized data virtualization of relational sources, such as Azure SQL Database or Azure Synapse Analytics, allows you to access all your data. Your queries are automatically optimized by moving processing closer to the source data, which maximizes performance while minimising latency.

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.

Instead of getting bogged down in data warehouse code, keep your eyes on the important and compelling reporting, analytics, and insights. Your data warehouse should not become a mess of hundreds of unmanageable stored procedures, notebooks, stored processes, tables, and other complicated pieces. Views and other information. The effort required to design, build and manage a data warehouse is dramatically reduced with Dimodelo DW Studio. You can design, build, and deploy a data warehouse that targets Azure Synapse Analytics. Dimodelo Data Warehouse Studio creates a best-practice architecture using Azure Data Lake, Polybase, and Azure Synapse Analytics. This results in a modern, high-performance data warehouse in the cloud. Dimodelo Data Warehouse Studio creates a best-practice architecture that delivers a modern, high-performance data warehouse in the cloud by using parallel bulk loads and in memory tables.

Cazena's Instant Data Lake reduces the time it takes to analyze and implement AI/ML. It can be done in minutes instead of months. Cazena's patented automated data platform powers the first SaaS experience with data lakes. Zero operations are required. Enterprises require a data lake that can easily store all their data and tools for machine learning, analytics, and AI. A data lake must provide secure data ingestion, flexible storage, access and identity management, optimization, tool integration, and other features to be effective. Cloud data lakes can be difficult to manage by yourself. This is why expensive teams are required. Cazena's Instant Cloud Data Lakes can be used immediately for data loading and analysis. Everything is automated and supported by Cazena's SaaS platform with continuous Ops, self-service access via Cazena SaaS Console. Cazena's Instant Data Lakes can be used for data storage, analysis, and secure data ingest.

SelectDB is an advanced data warehouse built on Apache Doris. It supports rapid query analysis of large-scale, real-time data. Clickhouse to Apache Doris to separate the lake warehouse, and upgrade the lake storage. Fast-hand OLAP system carries out nearly 1 billion queries every day in order to provide data services for various scenes. The original lake warehouse separation was abandoned due to problems with storage redundancy and resource seizure. Also, it was difficult to query and adjust. It was decided to use Apache Doris lakewarehouse, along with Doris's materialized views rewriting capability and automated services to achieve high-performance query and flexible governance. Write real-time data within seconds and synchronize data from databases and streams. Data storage engine with real-time update and addition, as well as real-time polymerization.

You can manage your storage accounts across multiple subscriptions in all Azure regions, Azure Stack and Azure Government. To manage more cloud storage, you can add new capabilities and features with extensions. A graphical user interface (GUI), which is intuitive, easy-to-use, and rich in features, allows for full control of cloud storage resources. Azure AD allows you to securely access your data with fine-tuned access control lists (ACL) permissions. Connect and manage your Azure storage service resources and accounts across subscriptions and organisations efficiently. Azure Storage, Azure Data Lake Storage, as well as Azure managed disks, can be created, deleted, viewed, edited, and managed. An intuitive interface allows you to view, search, and interact seamlessly with your data and resources. Accessibility is improved with multiple screen reader options, high-contrast themes, and hot key on Windows and macOS.

You can load your data into Hadoop or data lakes. Prepare it for visualizations, advanced analytics, reports and reporting - all from the data lakes. You can do it all yourself, fast and easily. It makes it easy to access, transform, and manage data stored in Hadoop/data lakes using a web-based interface. This reduces training requirements. It was designed from the ground up to manage large amounts of data in Hadoop and data lakes. It is not repurposed or adapted from existing IT-focused tools. You can group multiple directives together to run simultaneously, or one after another. The exposed Public API allows you to schedule and automate directives. Allows you to share or secure directives. These directives can be called from SAS Data Integration Studio. This combines technical and non-technical user activities. Included directives: casing, gender, pattern analysis, field extract, match-merge, cluster-survive. For better performance, profiling runs parallel on the Hadoop cluster.

Metadata, such as instrument settings, last day of service, experiment time, and other information, are not tracked. Raw data is lost and analyses cannot be modified or run again without significant effort. Meta-analyses are difficult because of the lack of traceability. Scientists' productivity suffers even if they have to enter the primary analysis results. Raw data is stored in the cloud, and analysis can be automated with traceability. Data can then be used to create ELNs/LIMSs, Excel, analysis apps, and pipelines - any kind of data. This data lake is also created as we go. Your raw data, analyzed and metadata as well as any internal data from intergrated apps are all saved in one cloud data lake. Run automatic analyses and add metadata automatically. Push results into any app, pipeline, or back to instruments for control.

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.

Tarsal is infinitely scalable, so as your company grows, Tarsal will grow with you. Tarsal allows you to easily switch from SIEM data to data lake data with just one click. Keep your SIEM, and migrate analytics to a data-lake gradually. Tarsal doesn't require you to remove anything. Some analytics won't work on your SIEM. Tarsal can be used to query data in a data lake. Your SIEM is a major line item in your budget. Tarsal can be used to send some of this data to your data lake. Tarsal is a highly scalable ETL pipeline designed for security teams. With just a few mouse clicks you can easily exfiltrate terabytes with instant normalization and route the data to your destination.

Storage consumption in Azure is increasing at an amazing rate, even faster than originally anticipated. Organizations have a growing data footprint and are eager to take advantage Azure's unlimited storage and other resources. It's easy for organizations to lose track of their storage needs as they grow. This can lead to cost blowout and a higher storage cost. Cloud Storage Manager will allow you to instantly see where your storage is, which will allow you to take back control of your storage and save money. Cloud Storage Manager gives you an Azure Explorer-like view of all Azure Blobs and Azure Files. This view allows you to see the details of each Blob, including its size, date created, and last modified, along with the Storage Tiering it is currently in.

NooBaa, a software-driven infrastructure, enables agility and flexibility while enabling hybrid cloud capabilities. From download to operational system, a deployment takes just 5 minutes. NooBaa is a new way to manage the rapid growth of data. It offers unprecedented flexibility, pay as you go pricing, and remarkable management simplicity. NooBaa is able to consume data from AWS S3, Microsoft Cloud Blobs, Google Storage, or any other AWS S3 compatible storage Private Cloud. You can eliminate vendor lock-in by allowing your application stack to be independent from the underlying infrastructure. This independence allows for the interoperability needed to quickly migrate or expand workloads. It allows you to run a particular workload on a specific platform without worrying about storage. NooBaa offers an AWS S3 compatible API. This API is the de facto standard and independent of any particular vendor or location.

By deliberately introducing faults to simulate real-world outages, chaos engineering and testing can improve application resilience. Azure Chaos Studio is an experimentation platform that allows you to quickly find problems in late-stage development and production. Disrupt your apps deliberately to identify gaps and plan mitigations, before your customers experience a problem. To better understand application resilience, subject your Azure apps in a controlled way to faults that are real or simulated. With chaos engineering and testing, you can observe how your apps respond to real-world disruptions, such as network latency or an unexpected storage failure, expiring secrets or even a complete data center outage. Validate product quality where and when it makes sense for your company. Use a hypothesis-based method to improve application resilience by integrating chaos into your CI/CD pipeline.

Apache Doris is an advanced data warehouse for real time analytics. It delivers lightning fast analytics on real-time, large-scale data. Ingestion of micro-batch data and streaming data within a second. Storage engine with upserts, appends and pre-aggregations in real-time. Optimize for high-concurrency, high-throughput queries using columnar storage engine, cost-based query optimizer, and vectorized execution engine. Federated querying for data lakes like Hive, Iceberg, and Hudi and databases like MySQL and PostgreSQL. Compound data types, such as Arrays, Maps and JSON. Variant data types to support auto datatype inference for JSON data. NGram bloomfilter for text search. Distributed design for linear scaling. Workload isolation, tiered storage and efficient resource management. Supports shared-nothing as well as the separation of storage from compute.

ICONICS' Hyper historian™, a 64-bit high-speed, reliable and robust historian, is advanced 64-bit. Hyper Historian's high compression algorithm provides exceptional performance and efficient use of resources. It is designed for mission-critical applications. Hyper Historian can be integrated with our ISA 95-compliant asset databank and the most recent big data technologies, such as Azure SQL, Microsoft Data Lakes and Kafka. Hyper Historian is the most secure and efficient real-time plant historian available for any Microsoft operating system. Hyper Historian has a module that allows users to manually or automatically insert data. This allows users to import log data from other historians, databases, and intermittently connected field devices or equipment. This greatly increases the reliability of data capture, even in the face of network disruptions. Leverage rapid collection for enterprise-wide storage.

Customers can deploy Hadoop clusters in any size using Oracle Big Data Service. VM shapes range from 1 OCPU up to a dedicated bare-metal environment. Customers can choose between high-performance block storage or cost-effective block store, and can grow and shrink their clusters. Create Hadoop-based data lakes quickly to expand or complement customer data warehouses and ensure that all data can be accessed and managed efficiently. The included notebook supports R, Python, and SQL. Data scientists can query, visualize, and transform data to build machine learning models. Transfer customer-managed Hadoop clusters from a managed cloud-based service to improve resource utilization and reduce management costs.

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.

Etleap was created on AWS to support Redshift, snowflake and S3/Glue data warehouses and data lakes. Their solution simplifies and automates ETL through fully-managed ETL as-a-service. Etleap's data wrangler allows users to control how data is transformed for analysis without having to write any code. Etleap monitors and maintains data pipes for availability and completeness. This eliminates the need for constant maintenance and centralizes data sourced from 50+ sources and silos into your database warehouse or data lake.

Archon Data Store™ is an open-source archive lakehouse platform that allows you to store, manage and gain insights from large volumes of data. Its minimal footprint and compliance features enable large-scale processing and analysis of structured and unstructured data within your organization. Archon Data Store combines data warehouses, data lakes and other features into a single platform. This unified approach eliminates silos of data, streamlining workflows in data engineering, analytics and data science. Archon Data Store ensures data integrity through metadata centralization, optimized storage, and distributed computing. Its common approach to managing data, securing it, and governing it helps you innovate faster and operate more efficiently. Archon Data Store is a single platform that archives and analyzes all of your organization's data, while providing operational efficiencies.

Amazon Comprehend medical allows for easy searching and querying of unstructured data. Amazon Athena queries and Amazon SageMaker models combined with Amazon QuickSight analytics can be used to make predictions about health data. Support interoperable standards, such as Fast Healthcare Interoperability Resources. Cloud-based medical imaging applications can be used to increase scale and lower costs. AWS HealthLake, a HIPAA eligible service, offers healthcare and life sciences organizations a chronological view on individual or patient health data to query and analyze at scale. Advanced analytics tools and machine learning models can be used to analyze population health trends, predict outcome, and manage costs. With a longitudinal view on patient journeys, identify opportunities to close gaps in the care and deliver targeted intervention. Apply advanced analytics and machine learning to newly structured data in order to optimize appointment scheduling and to reduce unnecessary procedures.

Our customers can leverage their data fabric to achieve remarkable outcomes in automation, data-driven decision making and other areas. We provide a complete view into the data supply chain through ingestion, indexing and data remediations. This allows businesses to scale, increase their profitability, and realize measurable impact. Through a robust, scalable data pipeline, we can ingestion data from client data lakes to secure storage systems. Advanced image classification techniques and NLP techniques allow for quick validation, classification, and categorizing of scanned documents. The user can search the data using a query interface and personalizable dashboard. The results will be presented in readable format so that they can bookmark, filter, or restructure the view.

Open core, delivering hybrid cloud and multi-cloud integration Data Fusion is built with open source project CDAP. This open core allows users to easily port data from their projects. Cloud Data Fusion users can break down silos and get insights that were previously unavailable thanks to CDAP's integration with both on-premises as well as public cloud platforms. Integrated with Google's industry-leading Big Data Tools Data Fusion's integration to Google Cloud simplifies data security, and ensures that data is instantly available for analysis. Cloud Data Fusion integration makes it easy to develop and iterate on data lakes with Cloud Storage and Dataproc.

Many services and applications that run in public clouds use Amazon S3 buckets or Azure Blob storage. Storage can become infected with malware over time. Misconfigured buckets can lead to data breaches. Unclassified sensitive data can also result in compliance violations and fines. CWP for Storage scans Amazon S3 buckets, Azure Blobs and other cloud storage to ensure that it is secure and clean. CWP for Storage DLP applies Symantec DLP policies to Amazon S3 in order to classify and discover sensitive information. AWS Tags are available for use in remediation and other actions. Cloud security posture management (CSPM), for Amazon Web Services (AWS), Microsoft Azure (M Azure) and Google Cloud Platform(GCP). While containers improve agility, they also introduce security vulnerabilities and public cloud security challenges that can increase risk.

PuppyGraph allows you to query multiple data stores in a single graph model. Graph databases can be expensive, require months of setup, and require a dedicated team. Traditional graph databases struggle to handle data beyond 100GB and can take hours to run queries with multiple hops. A separate graph database complicates architecture with fragile ETLs, and increases your total cost ownership (TCO). Connect to any data source, anywhere. Cross-cloud and cross region graph analytics. No ETLs are required, nor is data replication. PuppyGraph allows you to query data as a graph directly from your data lakes and warehouses. This eliminates the need for time-consuming ETL processes that are required with a traditional graph databases setup. No more data delays or failed ETL processes. PuppyGraph eliminates graph scaling issues by separating computation from storage.

SAP IQ, our columnar database management system (RDBMS), is optimized for Big Data analytics to enhance in-the-moment decision making.

Built on the foundation of real AI, Quantarium's innovative-yet-explainable solutions enable more accurate decision making, comprehensively spanning valuations, analytics, propensity models and portfolio optimization. Real estate insights that provide the most accurate information about property trends and values. Industry-leading, highly scalable, resilient and next-generation cloud infrastructure. Quantarium's adaptive AI computer-vision technology is trained from millions of real estate photos and then integrated into a variety of QVM-based solutions. Our managed data set, which is an asset in the Quantarium Data lake, is the most dynamic and comprehensive in the real estate industry. This data set is machine-generated and AI-enhanced. It was curated by AI scientists and data scientists, as well as software engineers and industry experts. It is the new standard for real estate information. Quantarium combines deep domain knowledge, self-learning technology, innovative computer vision, and a wealth of industry expertise.

Trino is an engine that runs at incredible speeds. Fast-distributed SQL engine for big data analytics. Helps you explore the data universe. Trino is an extremely parallel and distributed query-engine, which is built from scratch for efficient, low latency analytics. Trino is used by the largest organizations to query data lakes with exabytes of data and massive data warehouses. Supports a wide range of use cases including interactive ad-hoc analysis, large batch queries that take hours to complete, and high volume apps that execute sub-second queries. Trino is a ANSI SQL query engine that works with BI Tools such as R Tableau Power BI Superset and many others. You can natively search data in Hadoop S3, Cassandra MySQL and many other systems without having to use complex, slow and error-prone copying processes. Access data from multiple systems in a single query.

Storage that does not lock data in. Managed data lakes allow you to get up and running quickly. You don't need to be a data expert to store, retrieve, and access data. Cribl Lake prevents you from drowning in information. Store, manage, enforce policies on data, and access it when you need to. Open formats and unified policies for retention, security and access control will help you to embrace the future. Let Cribl do the heavy lifting to make data usable and valuable for the teams and tools who need it. Cribl Lake allows you to be up and running in minutes, not months. Zero configuration thanks to automated provisioning and pre-built integrations. Streamline workflows using Stream and Edge to streamline data ingestion and routing. Cribl Search allows you to get the most out of your data, no matter where it is stored. You can easily collect and store your data for long-term storage. Define specific retention periods to comply with legal and business requirements.

Locus offers multiple environments for geospatial analysis. This makes it easy for anyone, from tech-challenged marketers to deep query analysis for data scientists, analysts, and top-level metrics to data-driven executives looking to make their next big move. This makes it easy to connect data sources to LOCUS. Connection Hub integrates data lineage governance, transformation capabilities built-in for integration with tools like LOCUS Notebook or LOCUS QL. EQ uses Apache Airflow to build its own directed acyclical graph processing framework. The DAG Builder is designed to crunch (and chew) your geospatial workflows. It has over twenty (20) helper stages.

We've been working on Generative AI for 5 years. Deep Lake combines the power and flexibility of vector databases and data lakes to create enterprise-grade LLM-based solutions and refine them over time. Vector search does NOT resolve retrieval. You need a serverless search for multi-modal data including embeddings and metadata to solve this problem. You can filter, search, and more using the cloud, or your laptop. Visualize your data and embeddings to better understand them. Track and compare versions to improve your data and your model. OpenAI APIs are not the foundation of competitive businesses. Your data can be used to fine-tune LLMs. As models are being trained, data can be efficiently streamed from remote storage to GPUs. Deep Lake datasets can be visualized in your browser or Jupyter Notebook. Instantly retrieve different versions and materialize new datasets on the fly via queries. Stream them to PyTorch, TensorFlow, or Jupyter Notebook.

Cloud-integrated hybrid storage can be created that integrates with your existing network-attached storage and Azure Blob Storage. This appliance optimizes data access in your datacenter, in Azure or across a wide area network (WAN). Microsoft Azure FXT Edge filter is a combination of software and hardware. It provides high throughput and low latency to support hybrid storage infrastructure that supports high-performance computing (HPC). Scale-out clustering allows for non-disruptive NAS performance scale-up. To scale to millions of IOPS, and hundreds of gigabytes/s, join up to 24 FXT cluster nodes. Azure FXT Edge filter is the best choice for file-based workloads that require performance and scale. Azure FXT Edge Filer makes it easy to manage data storage. To keep your data accessible and available with minimal latency, you can transfer aging data to Azureblob Storage. Balance cloud and on-premise storage

MovingLake offers state-of the-art data connectors that provide real-time data for infrastructure, hospitality and e-commerce. You can power your data warehouses, databases, data lakes, and microservices with the same API connectors and get consistent data across all of your systems. MovingLake makes it easier to make data-driven decisions quicker!

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.

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

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.

JetStream DR simplifies the process of continuously protecting data center applications, while minimizing downtime and lowering operating costs. It also enables a shift from CapEx into OpEx via an on-demand subscription. JetStream DR implements continuous data protection (CDP) through the constant replication of data into cost-effective Azure NetApp Files and Azure Blob Storage. This allows JetStream DR to scale independent from compute resources without compromising performance. Hypervisor-based, real-time replication for continuous data security. JetStream DR does not require snapshots. This means that application performance remains high, even though JetStream DR provides near-zero RPO. Data transfer is resilient against network interruptions. This ensures that VM protection continues despite network outages or interference.