Alternatives to BigLake

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

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.

AWS Lake Formation makes it simple to create a secure data lake in a matter of days. A data lake is a centrally managed, secured, and curated repository that stores all of your data. It can be both in its original form or prepared for analysis. Data lakes allow you to break down data silos, combine different types of analytics, and gain insights that will guide your business decisions. It is a time-consuming, manual, complex, and tedious task to set up and manage data lakes. This includes loading data from different sources, monitoring data flows, setting partitions, turning encryption on and managing keys, defining and monitoring transformation jobs, reorganizing data in a columnar format, deduplicating redundant information, and matching linked records. Once data has been loaded into a data lake, you will need to give fine-grained access and audit access over time to a wide variety of analytics and machine learning tools and services.

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.

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.

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.

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.

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.

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.

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.

Amazon Security Lake centralizes all security data, including data from AWS, SaaS, on-premises and cloud sources, into a data lake that is stored in your account. Security Lake allows you to gain a better understanding of all your security data throughout your organization. You can also improve your workloads, apps, and data. Security Lake has adopted an open standard, the Open Cybersecurity Schema Framework. The service can combine and normalize security data from AWS as well as a wide range of enterprise data sources with OCSF support. You can use your favorite analytics tools to analyze security data, while maintaining complete control and ownership of that data. Centralize data visibility across all your accounts and AWS regions. Normalizing your security data according to an open standard will streamline your data management.

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.

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.

Hydrolix is a streaming lake of data that combines decoupled archiving, indexed searching, and stream processing for real-time query performance on terabyte scale at a dramatically lower cost. CFOs love that data retention costs are 4x lower. Product teams appreciate having 4x more data at their disposal. Scale up resources when needed and down when not. Control costs by fine-tuning resource consumption and performance based on workload. Imagine what you could build if you didn't have budget constraints. Log data from Kafka, Kinesis and HTTP can be ingested, enhanced and transformed. No matter how large your data, you will only get the data that you need. Reduce latency, costs, and eliminate timeouts and brute-force queries. Storage is decoupled with ingest and queries, allowing them to scale independently to meet performance and cost targets. Hydrolix's HDX (high-density compress) reduces 1TB to 55GB.

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.

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.

Secure and manage the data lifecycle, from Edge to AI in any cloud or data centre. Operates on all major public clouds as well as the private cloud with a public experience everywhere. Integrates data management and analytics experiences across the entire data lifecycle. All environments are covered by security, compliance, migration, metadata management. Open source, extensible, and open to multiple data stores. Self-service analytics that is faster, safer, and easier to use. Self-service access to multi-function, integrated analytics on centrally managed business data. This allows for consistent experiences anywhere, whether it is in the cloud or hybrid. You can enjoy consistent data security, governance and lineage as well as deploying the cloud analytics services that business users need. This eliminates the need for shadow IT solutions.

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.

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.

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.

NewEvol is a technologically advanced product suite that uses advanced analytics and data science to identify anomalies in data. NewEvol is a powerful tool that can be used to compile data for small and large enterprises. It supports rule-based alerting, visualization, automation, and responses. NewEvol is a robust system that can handle challenging business requirements. NewEvol Expertise 1. Data Lake 2. SIEM 3. SOAR 4. Threat Intelligence 5. Analytics

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.

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.

A single storage platform can eliminate data silos. Tiered storage and policy management can help you reduce costs. Azure Active Directory (Azure AD), and role-based access control(RBAC) can authenticate data. You can also help protect your data with advanced threat protection and encryption at rest. Flexible mechanisms provide protection for data access, encryption, network-level control, and more. Highly secure. A single storage platform that supports all the most popular analytics frameworks. Cost optimization through independent scaling of storage, compute, lifecycle management and object-level Tiering. With the Azure global infrastructure, you can meet any capacity requirement and manage data with ease. Large-scale analytics queries run at high performance.

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.

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.

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.

iomete platform combines a powerful lakehouse with an advanced data catalog, SQL editor and BI, providing you with everything you need to become data-driven.

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.

Big data is essential for solving today's industry and enterprise problems. The ability to capture data from across your enterprise--whether generated by disparate applications, people, or IoT infrastructure-offers tremendous potential. Data Lake tools from Infor provide schema-on-read intelligence and a flexible data consumption framework that enables new ways to make key decisions. You can use leveraged access to all of your Infor ecosystem to start capturing and delivering large data to power your next generation machine learning and analytics strategies. The Infor Data Lake is infinitely scalable and provides a central repository for all your enterprise data. You can grow with your insights and investments, ingest additional content for better informed decision making, improve your analytics profiles and provide rich data sets that will enable you to build more powerful machine-learning processes.

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.

Apache Hadoop is a software library that allows distributed processing of large data sets across multiple computers. It uses simple programming models. It can scale from one server to thousands of machines and offer local computations and storage. Instead of relying on hardware to provide high-availability, it is designed to detect and manage failures at the application layer. This allows for highly-available services on top of a cluster computers that may be susceptible to failures.

Palo Alto Networks solutions can be enabled by integrating security data from your enterprise. Rapidly simplify security operations by integrating, transforming, and collecting your enterprise's security information. Access to rich data at cloud native scale enables AI and machine learning. Using trillions of multi-source artifacts, you can significantly improve detection accuracy. Cortex XDR™, the industry's leading prevention, detection, response platform, runs on fully integrated network, endpoint, and cloud data. Prisma™, Access protects applications, remote networks, and mobile users in a consistent way, no matter where they are. All users can access all applications via a cloud-delivered architecture, regardless of whether they are at headquarters, branch offices, or on the road. Combining Panorama™, Cortex™, and Data Lake management creates an affordable, cloud-based log solution for Palo Alto Networks Next-Generation Firewalls. Cloud scale, zero hardware, available anywhere.

Scalytics Connect combines data mesh and in-situ data processing with polystore technology, resulting in increased data scalability, increased data processing speed, and multiplying data analytics capabilities without losing privacy or security. You take advantage of all your data without wasting time with data copy or movement, enable innovation with enhanced data analytics, generative AI and federated learning (FL) developments. Scalytics Connect enables any organization to directly apply data analytics, train machine learning (ML) or generative AI (LLM) models on their installed data architecture.

Tabular is a table store that allows you to create an open table. It was created by the Apache Iceberg creators. Connect multiple computing frameworks and engines. Reduce query time and costs up to 50%. Centralize enforcement of RBAC policies. Connect any query engine, framework, or tool, including Athena BigQuery, Snowflake Databricks Trino Spark Python, Snowflake Redshift, Snowflake Databricks and Redshift. Smart compaction, data clustering and other automated services reduce storage costs by up to 50% and query times. Unify data access in the database or table. RBAC controls are easy to manage, enforce consistently, and audit. Centralize your security at the table. Tabular is easy-to-use and has RBAC, high-powered performance, and high ingestion under the hood. Tabular allows you to choose from multiple "best-of-breed" compute engines, based on their strengths. Assign privileges to the data warehouse database or table level.

lakeFS allows you to manage your data lake in the same way as your code. Parallel pipelines can be used for experimentation as well as CI/CD of your data. This simplifies the lives of data scientists, engineers, and analysts who work in data transformation. lakeFS is an open-source platform that provides resilience and manageability for object-storage-based data lakes. lakeFS allows you to build repeatable, atomic, and versioned data lakes operations. This includes complex ETL jobs as well as data science and analysis. lakeFS is compatible with AWS S3, Azure Blob Storage, and Google Cloud Storage (GCS). It is API compatible to S3 and seamlessly integrates with all modern data frameworks like Spark, Hive AWS Athena, Presto, AWS Athena, Presto, and others. lakeFS is a Git-like branching/committing model that can scale to exabytes by using S3, GCS, and Azure Blob storage.

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.

Datametica's birds have unmatched capabilities, which help to eliminate business risks, time, frustration, anxiety, and cost from the entire process for data warehouse migration to cloud. Datametica's automated product suite allows you to migrate existing data warehouses, data lakes, ETL, Enterprise business intelligence, and other data to the cloud environment of choice. Designing an end to end migration strategy that includes workload discovery, assessment and planning. From the discovery and assessment of your data warehouse to the planning of the migration strategy, Eagle provides clarity on what needs to be migrated, in what order, how to streamline the process, and what the costs and timelines are. The integrated view of the workloads and planning minimizes migration risk without affecting the business.

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.

End-to-end DataOps built upon an agile platform that protects and improves your data assets. Arena is the leading augmented data management platform. Our active data catalog allows for self-service data enrichment to control complex data environments. You can create custom workflows to increase the reliability and accuracy of each data set. Machine-learning can be used to identify and align master assets for better data decisions. Superior security is assured with complete lineage, including detailed visualizations and masking. Data management is easy with Arena. Arena can catalog your data from any location. Our extensible connections allow for analytics across all your preferred tools. Overcome data sprawl challenges with our software. Our software is designed to drive business and analytics success, while also providing the controls and extensibility required in today's multicloud data complexity.

Utilihive, a cloud-native big-data integration platform, is offered as a managed (SaaS) service. Utilihive, the most popular Enterprise-iPaaS (iPaaS), is specifically designed for utility and energy usage scenarios. Utilihive offers both the technical infrastructure platform (connectivity and integration, data ingestion and data lake management) and preconfigured integration content or accelerators. (connectors and data flows, orchestrations and utility data model, energy services, monitoring and reporting dashboards). This allows for faster delivery of data-driven services and simplifies operations.

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.

Mitzu.io is a warehouse-native analytics platform tailored for SaaS and e-commerce teams, enabling actionable insights directly from data warehouses or lakes like Snowflake, BigQuery, and Redshift. It eliminates complex data modeling and duplication by working on raw datasets, ensuring streamlined workflows. Mitzu’s standout feature is self-service analytics, empowering non-technical users like marketers and product managers to explore data without SQL expertise. It auto-generates SQL queries based on user interactions for real-time insights into user behavior and engagement. Plus, seat-based pricing is a cost-effective alternative to traditional tools.

Businesses must adapt quickly to meet changing customer preferences and requirements. Sprinkle is an agile analytics platform that helps you meet changing customer needs. Sprinkle was created with the goal of simplifying end-to-end data analytics for organisations. It allows them to integrate data from multiple sources, change schemas, and manage pipelines. We created a platform that allows everyone in the organization to search and dig deeper into data without having to have any technical knowledge. Our team has extensive experience with data and built analytics systems for companies such as Yahoo, Inmobi, Flipkart. These companies are able to succeed because they have dedicated teams of data scientists, business analysts, and engineers who produce reports and insights. We discovered that many organizations struggle to access simple self-service reporting and data exploration. We set out to create a solution that would allow all companies to leverage data.

Qwak build system allows data scientists to create an immutable, tested production-grade artifact by adding "traditional" build processes. Qwak build system standardizes a ML project structure that automatically versions code, data, and parameters for each model build. Different configurations can be used to build different builds. It is possible to compare builds and query build data. You can create a model version using remote elastic resources. Each build can be run with different parameters, different data sources, and different resources. Builds create deployable artifacts. Artifacts built can be reused and deployed at any time. Sometimes, however, it is not enough to deploy the artifact. Qwak allows data scientists and engineers to see how a build was made and then reproduce it when necessary. Models can contain multiple variables. The data models were trained using the hyper parameter and different source code.

Create data products in seconds from any source, without moving data. You can make them available to anyone while still maintaining total control. Deliver powerful experiences to unlock the value. Enhance your data mesh through seamless sharing, discovery, and governance of data across domains. Unified access to high-quality products will accelerate innovation and foster collaboration. Access AI models for all users. Control the way data interacts with AI in order to protect intellectual property. Automate AI workflows for rapid integration and iteration of new capabilities. Snowflake allows you to access and build data products without having to move any data. Enjoy the ease of getting even more out of your data. Allow anyone to easily analyze data, and eliminate the need for central provisioning of infrastructure and software. Data products are seamlessly integrated with tools to ensure governance and speed up outcomes.

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.

Our AI-powered Intelligent Data Platform, which is modular and comprehensive, is the best in the industry. It allows you to unlock the potential of data in your enterprise and empowers you with the ability to solve complex problems. Our platform sets a new standard in enterprise-class data management. We offer best-in-class products, and an integrated platform that unifies them. This allows you to power your business with intelligent information. You can connect to any data source, and scale with confidence. A global platform processes more than 15 trillion cloud transactions each month. A global platform that delivers trusted data at scale across all data management use cases will help you future-proof your business. Our AI-powered architecture supports integration patterns, allowing you to grow and develop at your own pace. Our solution is modular and API-driven.