Alternatives to SHREWD Platform

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

Gartner's Magic Quadrant includes Centralpoint as a Digital Experience Platform. It is used by more than 350 clients around the world, and it goes beyond Enterprise Content Management. It securely authenticates (AD/SAML/OpenID, oAuth), all users for self-service interaction. Centralpoint automatically aggregates information from different sources and applies rich metadata against your rules to produce true Knowledge Management. This allows you to search for and relate disparate data sets from anywhere. Centralpoint's Module Gallery is the most robust and can be installed either on-premise or in the cloud. Check out our solutions for Automating Metadata and Automating Retention Policy Management. We also offer solutions to simplify the mashup of disparate data to benefit from AI (Artificial Intelligence). Centralpoint is often used to provide easy migration tools and an intelligent alternative to Sharepoint. It can be used to secure portal solutions for public sites, intranets, members, or extranets.

Cazena's Instant Data Lake significantly reduces the time needed for analytics and AI/ML from several months to just a few minutes. Utilizing its unique automated data platform, Cazena introduces a pioneering SaaS model for data lakes, requiring no operational input from users. Businesses today seek a data lake that can seamlessly accommodate all their data and essential tools for analytics, machine learning, and artificial intelligence. For a data lake to be truly effective, it must ensure secure data ingestion, provide adaptable data storage, manage access and identities, facilitate integration with various tools, and optimize performance among other features. Building cloud data lakes independently can be quite complex and typically necessitates costly specialized teams. Cazena's Instant Cloud Data Lakes are not only designed to be readily operational for data loading and analytics but also come with a fully automated setup. Supported by Cazena’s SaaS Platform, they offer ongoing operational support and self-service access through the user-friendly Cazena SaaS Console. With Cazena's Instant Data Lakes, users have a completely turnkey solution that is primed for secure data ingestion, efficient storage, and comprehensive analytics capabilities, making it an invaluable resource for enterprises looking to harness their data effectively and swiftly.

Fivetran is a comprehensive data integration solution designed to centralize and streamline data movement for organizations of all sizes. With more than 700 pre-built connectors, it effortlessly transfers data from SaaS apps, databases, ERPs, and files into data warehouses and lakes, enabling real-time analytics and AI-driven insights. The platform’s scalable pipelines automatically adapt to growing data volumes and business complexity. Leading companies such as Dropbox, JetBlue, Pfizer, and National Australia Bank rely on Fivetran to reduce data ingestion time from weeks to minutes and improve operational efficiency. Fivetran offers strong security compliance with certifications including SOC 1 & 2, GDPR, HIPAA, ISO 27001, PCI DSS, and HITRUST. Users can programmatically create and manage pipelines through its REST API for seamless extensibility. The platform supports governance features like role-based access controls and integrates with transformation tools like dbt Labs. Fivetran helps organizations innovate by providing reliable, secure, and automated data pipelines tailored to their evolving needs.

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

Delta Lake serves as an open-source storage layer that integrates ACID transactions into Apache Spark™ and big data operations. In typical data lakes, multiple pipelines operate simultaneously to read and write data, which often forces data engineers to engage in a complex and time-consuming effort to maintain data integrity because transactional capabilities are absent. By incorporating ACID transactions, Delta Lake enhances data lakes and ensures a high level of consistency with its serializability feature, the most robust isolation level available. For further insights, refer to Diving into Delta Lake: Unpacking the Transaction Log. In the realm of big data, even metadata can reach substantial sizes, and Delta Lake manages metadata with the same significance as the actual data, utilizing Spark's distributed processing strengths for efficient handling. Consequently, Delta Lake is capable of managing massive tables that can scale to petabytes, containing billions of partitions and files without difficulty. Additionally, Delta Lake offers data snapshots, which allow developers to retrieve and revert to previous data versions, facilitating audits, rollbacks, or the replication of experiments while ensuring data reliability and consistency across the board.

5X is a comprehensive data management platform that consolidates all the necessary tools for centralizing, cleaning, modeling, and analyzing your data. With its user-friendly design, 5X seamlessly integrates with more than 500 data sources, allowing for smooth and continuous data flow across various systems through both pre-built and custom connectors. The platform features a wide array of functions, including ingestion, data warehousing, modeling, orchestration, and business intelligence, all presented within an intuitive interface. It efficiently manages diverse data movements from SaaS applications, databases, ERPs, and files, ensuring that data is automatically and securely transferred to data warehouses and lakes. Security is a top priority for 5X, as it encrypts data at the source and identifies personally identifiable information, applying encryption at the column level to safeguard sensitive data. Additionally, the platform is engineered to lower the total cost of ownership by 30% when compared to developing a custom solution, thereby boosting productivity through a single interface that enables the construction of complete data pipelines from start to finish. This makes 5X an ideal choice for businesses aiming to streamline their data processes effectively.

Qubole stands out as a straightforward, accessible, and secure Data Lake Platform tailored for machine learning, streaming, and ad-hoc analysis. Our comprehensive platform streamlines the execution of Data pipelines, Streaming Analytics, and Machine Learning tasks across any cloud environment, significantly minimizing both time and effort. No other solution matches the openness and versatility in handling data workloads that Qubole provides, all while achieving a reduction in cloud data lake expenses by more than 50 percent. By enabling quicker access to extensive petabytes of secure, reliable, and trustworthy datasets, we empower users to work with both structured and unstructured data for Analytics and Machine Learning purposes. Users can efficiently perform ETL processes, analytics, and AI/ML tasks in a seamless workflow, utilizing top-tier open-source engines along with a variety of formats, libraries, and programming languages tailored to their data's volume, diversity, service level agreements (SLAs), and organizational regulations. This adaptability ensures that Qubole remains a preferred choice for organizations aiming to optimize their data management strategies while leveraging the latest technological advancements.

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

Varada offers a cutting-edge big data indexing solution that adeptly balances performance and cost while eliminating the need for data operations. This distinct technology acts as an intelligent acceleration layer within your data lake, which remains the central source of truth and operates within the customer's cloud infrastructure (VPC). By empowering data teams to operationalize their entire data lake, Varada facilitates data democratization while ensuring fast, interactive performance, all without requiring data relocation, modeling, or manual optimization. The key advantage lies in Varada's capability to automatically and dynamically index pertinent data, maintaining the structure and granularity of the original source. Additionally, Varada ensures that any query can keep pace with the constantly changing performance and concurrency demands of users and analytics APIs, while also maintaining predictable cost management. The platform intelligently determines which queries to accelerate and which datasets to index, while also flexibly adjusting the cluster to match demand, thereby optimizing both performance and expenses. This holistic approach to data management not only enhances operational efficiency but also allows organizations to remain agile in an ever-evolving data landscape.

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.

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.

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.

DataLux is an innovative platform designed for effective data management and analytics, specifically created to tackle various data-related issues while facilitating real-time decision-making. Equipped with user-friendly plug-and-play adaptors, it enables the aggregation of extensive data collections and offers the capability to collect and visualize insights instantaneously. Utilize the data lake to anticipate and drive new innovations, while ensuring that data is stored in a manner conducive to modeling. The platform allows for the development of portable applications by leveraging containerization, whether in a public cloud, private cloud, or on-premise environment. It seamlessly integrates diverse time-series market data and inferred information, including stock exchange tick data, market policy actions, relevant cross-industry news, and alternative datasets, to derive causal insights regarding stock markets and macroeconomic factors. By providing valuable insights, DataLux empowers businesses to shape their decisions and foster product innovations effectively. Additionally, it supports interdisciplinary A/B testing throughout the product development lifecycle, from initial ideation to final decision-making, ensuring a comprehensive approach to enhancing design and engineering processes.

Power BI provides advanced data analysis, leveraging AI features to transform complex datasets into visual insights. It integrates data into a single source, OneLake, reducing duplication and streamlining analysis. The platform enhances decision-making by integrating insights into everyday tools like Microsoft 365 and is bolstered by Microsoft Fabric for data team empowerment. Power BI is scalable, handling extensive data without performance loss, and integrates well with Microsoft's ecosystem for coherent data management. Its AI tools are user-friendly and contribute to efficient and accurate insights, supported by strong data governance measures. The Copilot function in Power BI enables quick and efficient report creation. Power BI Pro licenses individuals for self-service analytics, while the free account offers data connection and visualization capabilities. The platform ensures ease of use and accessibility, backed by comprehensive training. It has shown a notable return on investment and economic benefits, as reported in a Forrester study. Gartner's Magic Quadrant recognizes Power BI for its ability to execute and completeness of vision.

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

Easily create and execute highly parallel data transformation and processing tasks using U-SQL, R, Python, and .NET across vast amounts of data. With no need to manage infrastructure, you can process data on demand, scale up instantly, and incur costs only per job. Azure Data Lake Analytics allows you to complete big data tasks in mere seconds. There’s no infrastructure to manage since there are no servers, virtual machines, or clusters that require monitoring or tuning. You can quickly adjust the processing capacity, measured in Azure Data Lake Analytics Units (AU), from one to thousands for every job. Payment is based solely on the processing used for each job. Take advantage of optimized data virtualization for your relational sources like Azure SQL Database and Azure Synapse Analytics. Your queries benefit from automatic optimization, as processing is performed close to the source data without requiring data movement, thereby enhancing performance and reducing latency. Additionally, this setup enables organizations to efficiently utilize their data resources and respond swiftly to analytical needs.

Hydrolix serves as a streaming data lake that integrates decoupled storage, indexed search, and stream processing, enabling real-time query performance at a terabyte scale while significantly lowering costs. CFOs appreciate the remarkable 4x decrease in data retention expenses, while product teams are thrilled to have four times more data at their disposal. You can easily activate resources when needed and scale down to zero when they are not in use. Additionally, you can optimize resource usage and performance tailored to each workload, allowing for better cost management. Imagine the possibilities for your projects when budget constraints no longer force you to limit your data access. You can ingest, enhance, and transform log data from diverse sources such as Kafka, Kinesis, and HTTP, ensuring you retrieve only the necessary information regardless of the data volume. This approach not only minimizes latency and costs but also eliminates timeouts and ineffective queries. With storage being independent from ingestion and querying processes, each aspect can scale independently to achieve both performance and budget goals. Furthermore, Hydrolix's high-density compression (HDX) often condenses 1TB of data down to an impressive 55GB, maximizing storage efficiency. By leveraging such innovative capabilities, organizations can fully harness their data potential without financial constraints.

We are excited to introduce a groundbreaking big data platform designed for the music industry, which consolidates all relevant information into a single, reliable source to inform strategic decisions. Within the music business sector, numerous data sources exist, but they are often isolated and disjointed. Our innovative platform effectively identifies and integrates these sources, establishing a robust foundation of high-quality data applicable to everyday operations in the music industry. To operate effectively and securely while uncovering unique insights, record labels and agencies need an advanced data management and governance framework that ensures data is consistently accessible, pertinent, and practical. As data sources are integrated into Wavo’s Big Data Platform, machine learning techniques are utilized to categorize the data according to customized templates, facilitating easy access and deep dives into crucial information. This capability empowers every member of a music organization to harness and utilize data that is prepared and organized for immediate application and value creation. Ultimately, our platform serves as a catalyst for smarter decision-making and enhanced operational efficiency across the music business landscape.

An AI-driven self-service platform for advanced analytics allows users to connect diverse data sources and create visualizations effortlessly, eliminating the need for programming skills. This cloud-native solution delivers high-quality data and intelligent real-time insights across the organization with a no-code approach. Users can link various data sources, regardless of their formats, enabling the detection of underlying issues. The platform significantly reduces the time taken from sourcing to targeting data, while providing analytics accessible to those without technical expertise. With real-time data updates, users can connect any kind of data source, streaming it to a data lake at defined intervals, and visualize the information through sophisticated interactive dashboards. It combines descriptive, predictive, and prescriptive analytics in one platform, utilizing the capabilities of a search engine alongside advanced visualization techniques. There’s no need for conventional technology to explore data in multiple visualization styles. Users can easily manipulate data through roll-ups, slicing, and dicing, employing various mathematical computations directly within the Bizintel360 visualization environment, thus enhancing their analytical capabilities. This empowers businesses to make data-driven decisions with ease and speed.

The contemporary data workspace transforms the accessibility of your data assets, making everything from data tables to BI reports easily discoverable. With our robust search algorithms and user-friendly browsing experience, locating the right asset becomes effortless. Atlan simplifies the identification of poor-quality data through the automatic generation of data quality profiles. This includes features like variable type detection, frequency distribution analysis, missing value identification, and outlier detection, ensuring you have comprehensive support. By alleviating the challenges associated with governing and managing your data ecosystem, Atlan streamlines the entire process. Additionally, Atlan’s intelligent bots analyze SQL query history to automatically construct data lineage and identify PII data, enabling you to establish dynamic access policies and implement top-notch governance. Even those without technical expertise can easily perform queries across various data lakes, warehouses, and databases using our intuitive query builder that resembles Excel. Furthermore, seamless integrations with platforms such as Tableau and Jupyter enhance collaborative efforts around data, fostering a more connected analytical environment. Thus, Atlan not only simplifies data management but also empowers users to leverage data effectively in their decision-making processes.

biGENIUS automates all phases of analytic data management solutions (e.g. data warehouses, data lakes and data marts. thereby allowing you to turn your data into a business as quickly and cost-effectively as possible. Your data analytics solutions will save you time, effort and money. Easy integration of new ideas and data into data analytics solutions. The metadata-driven approach allows you to take advantage of new technologies. Advancement of digitalization requires traditional data warehouses (DWH) as well as business intelligence systems to harness an increasing amount of data. Analytical data management is essential to support business decision making today. It must integrate new data sources, support new technologies, and deliver effective solutions faster than ever, ideally with limited resources.

Dataleyk serves as a secure, fully-managed cloud data platform tailored for small and medium-sized businesses. Our goal is to simplify Big Data analytics and make it accessible to everyone. Dataleyk acts as the crucial link to achieve your data-driven aspirations. The platform empowers you to quickly establish a stable, flexible, and reliable cloud data lake, requiring minimal technical expertise. You can consolidate all of your company’s data from various sources, utilize SQL for exploration, and create visualizations using your preferred BI tools or our sophisticated built-in graphs. Transform your data warehousing approach with Dataleyk, as our cutting-edge cloud data platform is designed to manage both scalable structured and unstructured data efficiently. Recognizing data as a vital asset, Dataleyk takes security seriously by encrypting all your information and providing on-demand data warehousing options. While achieving zero maintenance may seem challenging, pursuing this goal can lead to substantial improvements in delivery and transformative outcomes. Ultimately, Dataleyk is here to ensure that your data journey is as seamless and efficient as possible.

iceDQ, a DataOps platform that allows monitoring and testing, is a DataOps platform. iceDQ is an agile rules engine that automates ETL Testing, Data Migration Testing and Big Data Testing. It increases productivity and reduces project timelines for testing data warehouses and ETL projects. Identify data problems in your Data Warehouse, Big Data, and Data Migration Projects. The iceDQ platform can transform your ETL or Data Warehouse Testing landscape. It automates it from end to end, allowing the user to focus on analyzing the issues and fixing them. The first edition of iceDQ was designed to validate and test any volume of data with our in-memory engine. It can perform complex validation using SQL and Groovy. It is optimized for Data Warehouse Testing. It scales based upon the number of cores on a server and is 5X faster that the standard edition.

Trino is a remarkably fast query engine designed to operate at exceptional speeds. It serves as a high-performance, distributed SQL query engine tailored for big data analytics, enabling users to delve into their vast data environments. Constructed for optimal efficiency, Trino excels in low-latency analytics and is extensively utilized by some of the largest enterprises globally to perform queries on exabyte-scale data lakes and enormous data warehouses. It accommodates a variety of scenarios, including interactive ad-hoc analytics, extensive batch queries spanning several hours, and high-throughput applications that require rapid sub-second query responses. Trino adheres to ANSI SQL standards, making it compatible with popular business intelligence tools like R, Tableau, Power BI, and Superset. Moreover, it allows direct querying of data from various sources such as Hadoop, S3, Cassandra, and MySQL, eliminating the need for cumbersome, time-consuming, and error-prone data copying processes. This capability empowers users to access and analyze data from multiple systems seamlessly within a single query. Such versatility makes Trino a powerful asset in today's data-driven landscape.

Optimize your time and reduce expenses by simplifying data pipelines using hassle-free open source solutions. Covering everything from data ingestion to visualization, all components are seamlessly integrated, fully managed, and exceptionally reliable, ensuring your engineering team enjoys working with data. You can opt for any of DoubleCloud’s managed open source services or take advantage of the entire platform's capabilities, which include data storage, orchestration, ELT, and instantaneous visualization. We offer premier open source services such as ClickHouse, Kafka, and Airflow, deployable on platforms like Amazon Web Services or Google Cloud. Our no-code ELT tool enables real-time data synchronization between various systems, providing a fast, serverless solution that integrates effortlessly with your existing setup. With our managed open-source data visualization tools, you can easily create real-time visual representations of your data through interactive charts and dashboards. Ultimately, our platform is crafted to enhance the daily operations of engineers, making their tasks more efficient and enjoyable. This focus on convenience is what sets us apart in the industry.

The Qlik Data Integration platform designed for managed data lakes streamlines the delivery of consistently updated, reliable, and trusted data sets for business analytics purposes. Data engineers enjoy the flexibility to swiftly incorporate new data sources, ensuring effective management at every stage of the data lake pipeline, which includes real-time data ingestion, refinement, provisioning, and governance. It serves as an intuitive and comprehensive solution for the ongoing ingestion of enterprise data into widely-used data lakes in real-time. Employing a model-driven strategy, it facilitates the rapid design, construction, and management of data lakes, whether on-premises or in the cloud. Furthermore, it provides a sophisticated enterprise-scale data catalog that enables secure sharing of all derived data sets with business users, thereby enhancing collaboration and data-driven decision-making across the organization. This comprehensive approach not only optimizes data management but also empowers users by making valuable insights readily accessible.

Doolytic is at the forefront of big data discovery, integrating data exploration, advanced analytics, and the vast potential of big data. The company is empowering skilled BI users to participate in a transformative movement toward self-service big data exploration, uncovering the inherent data scientist within everyone. As an enterprise software solution, doolytic offers native discovery capabilities specifically designed for big data environments. Built on cutting-edge, scalable, open-source technologies, doolytic ensures lightning-fast performance, managing billions of records and petabytes of information seamlessly. It handles structured, unstructured, and real-time data from diverse sources, providing sophisticated query capabilities tailored for expert users while integrating with R for advanced analytics and predictive modeling. Users can effortlessly search, analyze, and visualize data from any format and source in real-time, thanks to the flexible architecture of Elastic. By harnessing the capabilities of Hadoop data lakes, doolytic eliminates latency and concurrency challenges, addressing common BI issues and facilitating big data discovery without cumbersome or inefficient alternatives. With doolytic, organizations can truly unlock the full potential of their data assets.

Introducing a unique cloud data lakehouse that is entirely managed and capable of ingesting data from all your sources within minutes, while seamlessly accommodating every query engine at scale, all at a significantly reduced cost. This platform enables ingestion from both databases and event streams at terabyte scale in near real-time, offering the ease of fully managed pipelines. Furthermore, you can execute queries using any engine, catering to diverse needs such as business intelligence, real-time analytics, and AI/ML applications. By adopting this solution, you can reduce your expenses by over 50% compared to traditional cloud data warehouses and ETL tools, thanks to straightforward usage-based pricing. Deployment is swift, taking just minutes, without the burden of engineering overhead, thanks to a fully managed and highly optimized cloud service. Consolidate your data into a single source of truth, eliminating the necessity of duplicating data across various warehouses and lakes. Select the appropriate table format for each task, benefitting from seamless interoperability between Apache Hudi, Apache Iceberg, and Delta Lake. Additionally, quickly set up managed pipelines for change data capture (CDC) and streaming ingestion, ensuring that your data architecture is both agile and efficient. This innovative approach not only streamlines your data processes but also enhances decision-making capabilities across your organization.

Numerous customers of Amazon Web Services (AWS) seek a data storage and analytics solution that surpasses the agility and flexibility of conventional data management systems. A data lake has emerged as an innovative and increasingly favored method for storing and analyzing data, as it enables organizations to handle various data types from diverse sources, all within a unified repository that accommodates both structured and unstructured data. The AWS Cloud supplies essential components necessary for customers to create a secure, adaptable, and economical data lake. These components comprise AWS managed services designed to assist in the ingestion, storage, discovery, processing, and analysis of both structured and unstructured data. To aid our customers in constructing their data lakes, AWS provides a comprehensive data lake solution, which serves as an automated reference implementation that establishes a highly available and cost-efficient data lake architecture on the AWS Cloud, complete with an intuitive console for searching and requesting datasets. Furthermore, this solution not only enhances data accessibility but also streamlines the overall data management process for organizations.

SPECTRA is an innovative platform that leverages AI for data analytics and integration, enabling organizations to smartly gather, harmonize, process, and transfer data from various systems, thereby unlocking significant business value from diverse data sources. By centralizing data that is frequently dispersed across different applications and locations, it facilitates more seamless operations, quicker insights, and diminishes operational hurdles. SPECTRA also provides advanced services for data extraction and management, helping to establish scalable data lakes that act as a unified source of truth while modernizing data warehouses to enhance speed, efficiency, and analytical capabilities. It is capable of processing both structured and unstructured data and utilizes AI-enhanced analytics to aid businesses in extracting actionable insights, ultimately refining decision-making processes across various functions. Furthermore, SPECTRA streamlines analytics projects and bolsters research and development along with compliance efforts by integrating and standardizing data through technologies like optical character recognition and intelligent data labeling, which significantly enhances operational agility. In this way, organizations can better adapt to changing market demands and improve overall productivity.

Kyligence Zen can collect, organize, and analyze your metrics, so you can spend more time taking action. Kyligence Zen, the low-code metrics platform, is the best way to define, collect and analyze your business metrics. It allows users to connect their data sources quickly, define their business metrics in minutes, uncover hidden insights, and share these across their organization. Kyligence Enterprise offers a variety of solutions based on public cloud, on-premises, and private cloud. This allows enterprises of all sizes to simplify multidimensional analyses based on massive data sets according to their needs. Kyligence Enterprise based on Apache Kylin provides sub-second standard SQL queries based upon PB-scale datasets. This simplifies multidimensional data analysis for enterprises, allowing them to quickly discover the business value of massive amounts data and make better business decisions.

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

lakeFS allows you to control your data lake similarly to how you manage your source code, facilitating parallel pipelines for experimentation as well as continuous integration and deployment for your data. This platform streamlines the workflows of engineers, data scientists, and analysts who are driving innovation through data. As an open-source solution, lakeFS enhances the resilience and manageability of object-storage-based data lakes. With lakeFS, you can execute reliable, atomic, and versioned operations on your data lake, encompassing everything from intricate ETL processes to advanced data science and analytics tasks. It is compatible with major cloud storage options, including AWS S3, Azure Blob Storage, and Google Cloud Storage (GCS). Furthermore, lakeFS seamlessly integrates with a variety of modern data frameworks such as Spark, Hive, AWS Athena, and Presto, thanks to its API compatibility with S3. The platform features a Git-like model for branching and committing that can efficiently scale to handle exabytes of data while leveraging the storage capabilities of S3, GCS, or Azure Blob. In addition, lakeFS empowers teams to collaborate more effectively by allowing multiple users to work on the same dataset without conflicts, making it an invaluable tool for data-driven organizations.

QuerySurge is the smart Data Testing solution that automates the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Applications with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Big Data (Hadoop & NoSQL) Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise Application/ERP Testing Features Supported Technologies - 200+ data stores are supported QuerySurge Projects - multi-project support Data Analytics Dashboard - provides insight into your data Query Wizard - no programming required Design Library - take total control of your custom test desig BI Tester - automated business report testing Scheduling - run now, periodically or at a set time Run Dashboard - analyze test runs in real-time Reports - 100s of reports API - full RESTful API DevOps for Data - integrates into your CI/CD pipeline Test Management Integration QuerySurge will help you: - Continuously detect data issues in the delivery pipeline - Dramatically increase data validation coverage - Leverage analytics to optimize your critical data - Improve your data quality at speed

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

Kylo serves as an open-source platform designed for effective management of enterprise-level data lakes, facilitating self-service data ingestion and preparation while also incorporating robust metadata management, governance, security, and best practices derived from Think Big's extensive experience with over 150 big data implementation projects. It allows users to perform self-service data ingestion complemented by features for data cleansing, validation, and automatic profiling. Users can manipulate data effortlessly using visual SQL and an interactive transformation interface that is easy to navigate. The platform enables users to search and explore both data and metadata, examine data lineage, and access profiling statistics. Additionally, it provides tools to monitor the health of data feeds and services within the data lake, allowing users to track service level agreements (SLAs) and address performance issues effectively. Users can also create batch or streaming pipeline templates using Apache NiFi and register them with Kylo, thereby empowering self-service capabilities. Despite organizations investing substantial engineering resources to transfer data into Hadoop, they often face challenges in maintaining governance and ensuring data quality, but Kylo significantly eases the data ingestion process by allowing data owners to take control through its intuitive guided user interface. This innovative approach not only enhances operational efficiency but also fosters a culture of data ownership within organizations.

Yes Energy offers a wide array of power market data solutions that encompass both historical and real-time insights, all of which are thoroughly cleaned and standardized for user convenience. Their offerings include an assortment of products such as PowerSignals, QuickSignals, Live Power, DataSignals, Submission Services, Position Management, Demand Forecasts, EnCompass, PowerCore, and Marketplace, each tailored to address specific requirements within the energy industry. These innovative tools empower users to explore data via visually engaging interfaces, API data lakes, or cloud-hosted databases, thereby enhancing decision-making in the ever-evolving power market landscape. Whether you're in need of a dynamic dashboard to track market fluctuations or you prefer a comprehensive API, data lake, or cloud option to access the full spectrum of our data warehouse for advanced modeling, we are equipped with solutions to fulfill your diverse needs. Moreover, our commitment to providing high-quality data ensures that clients can trust the information they are utilizing to make critical business choices.

Currently, less than 10% of the vast $260 billion big data industry is being utilized, primarily due to outdated processes and the overpowering presence of intermediaries. Our goal is to democratize this market, enabling access to the remaining 90% of data that is currently untapped for sharing. We aim to establish a decentralized framework that creates a data oracle network, utilizing an open protocol that facilitates interaction among participants while fostering a sustainable economy. Our multifunctional decentralized application (DAPP) and crypto wallet provide users with the opportunity to earn rewards for the data they generate, alongside access to various decentralized finance (DeFi) tools through a seamless user experience. The GeoDB marketplace empowers data buyers globally to acquire data produced by users through applications linked to the GeoDB platform. Participants, known as data sources, contribute data that is uploaded via our proprietary and partner applications, while validators ensure the efficient transfer and verification of contracts through blockchain technology, allowing for a streamlined and decentralized process. This innovative approach not only enhances data accessibility but also promotes a collaborative environment for all stakeholders involved.

For contemporary leaders, embracing innovation and making daring choices is essential; however, the increasing complexity of systems has made critical data more elusive, hindering timely decision-making. Edify streamlines the management of higher education data by combining robust data warehousing technology with user-friendly analytics tools, offering a comprehensive solution to enhance digital excellence across campuses. Featuring a cloud-native, secure, and infinitely scalable data lake alongside a higher education data warehouse, Edify effectively supports vital institutional data initiatives. With the ability to ingest data through an extensive collection of pre-built connectors or custom extractions, Edify empowers teams to adopt best-practice rules and validations for education data management, ensuring a reliable single source of truth. At its foundation, Edify utilizes a transparent and education-focused data model that caters specifically to the needs of higher education. Additionally, Edify’s data governance tools are designed to maintain organization and accuracy within your data, ensuring that institutions can rely on well-structured information to drive their strategic goals. This comprehensive approach not only enhances data accessibility but also encourages informed decision-making across the institution.

Unlike most data management systems, PHEMI Health DataLab is built with Privacy-by-Design principles, not as an add-on. This means privacy and data governance are built-in from the ground up, providing you with distinct advantages: Lets analysts work with data without breaching privacy guidelines Includes a comprehensive, extensible library of de-identification algorithms to hide, mask, truncate, group, and anonymize data. Creates dataset-specific or system-wide pseudonyms enabling linking and sharing of data without risking data leakage. Collects audit logs concerning not only what changes were made to the PHEMI system, but also data access patterns. Automatically generates human and machine-readable de- identification reports to meet your enterprise governance risk and compliance guidelines. Rather than a policy per data access point, PHEMI gives you the advantage of one central policy for all access patterns, whether Spark, ODBC, REST, export, and more

Traditional Data Lakes frequently simplify their role to merely serving as inexpensive raw data repositories, overlooking crucial elements such as data transformation, quality assurance, and security protocols. Consequently, data scientists often find themselves dedicating as much as 80% of their time to the processes of data acquisition, comprehension, and cleansing, which delays their ability to leverage their primary skills effectively. Furthermore, the establishment of traditional Data Lakes tends to occur in isolation by various departments, each utilizing different standards and tools, complicating the implementation of cohesive analytical initiatives. In contrast, Smart Data Lakes address these challenges by offering both architectural and methodological frameworks, alongside a robust toolset designed to create a high-quality data infrastructure. Essential to any contemporary analytics platform, Smart Data Lakes facilitate seamless integration with popular Data Science tools and open-source technologies, including those used for artificial intelligence and machine learning applications. Their cost-effective and scalable storage solutions accommodate a wide range of data types, including unstructured data and intricate data models, thereby enhancing overall analytical capabilities. This adaptability not only streamlines operations but also fosters collaboration across different departments, ultimately leading to more informed decision-making.

Our platform allows businesses to use data, including its application in advanced analysis, machine learning and AI, to do great things without worrying that customers, employees or intellectual property are at risk. The Protegrity Data Protection Platform does more than just protect data. It also classifies and discovers data, while protecting it. It is impossible to protect data you don't already know about. Our platform first categorizes data, allowing users the ability to classify the type of data that is most commonly in the public domain. Once those classifications are established, the platform uses machine learning algorithms to find that type of data. The platform uses classification and discovery to find the data that must be protected. The platform protects data behind many operational systems that are essential to business operations. It also provides privacy options such as tokenizing, encryption, and privacy methods.

Apache DevLake (currently in incubation) consolidates, evaluates, and visualizes disparate data from various DevOps tools to extract valuable insights that promote engineering excellence. In the realm of software development, your data is often scattered across multiple silos and platforms. DevLake bridges these gaps, offering a holistic perspective on your Software Development Life Cycle (SDLC). The platform seamlessly implements metrics, from DORA to scrum retrospectives, with its preconfigured dashboards that align with widely-used frameworks and objectives. It is designed to accommodate teams of diverse sizes and structures, while also providing the flexibility to incorporate additional data sources, metrics, and dashboards through its adaptable data collection and transformation framework. You can easily select, modify, and schedule data synchronization from your preferred sources using the configuration interface. Gain insights by exploring pre-built dashboards tailored for various use cases and leverage the metrics to enhance your engineering processes. Additionally, you have the freedom to customize your own metrics and dashboards using SQL, thereby expanding the potential applications of DevLake. This versatility ensures that DevLake can evolve alongside your team's needs and the ever-changing demands of software development.

Hopsworks is a comprehensive open-source platform designed to facilitate the creation and management of scalable Machine Learning (ML) pipelines, featuring the industry's pioneering Feature Store for ML. Users can effortlessly transition from data analysis and model creation in Python, utilizing Jupyter notebooks and conda, to executing robust, production-ready ML pipelines without needing to acquire knowledge about managing a Kubernetes cluster. The platform is capable of ingesting data from a variety of sources, whether they reside in the cloud, on-premise, within IoT networks, or stem from your Industry 4.0 initiatives. You have the flexibility to deploy Hopsworks either on your own infrastructure or via your chosen cloud provider, ensuring a consistent user experience regardless of the deployment environment, be it in the cloud or a highly secure air-gapped setup. Moreover, Hopsworks allows you to customize alerts for various events triggered throughout the ingestion process, enhancing your workflow efficiency. This makes it an ideal choice for teams looking to streamline their ML operations while maintaining control over their data environments.