Alternatives to BigLake

Engineered for peak performance and efficient resource use, KrakenD can manage a staggering 70k requests per second on just one instance. Its stateless build ensures hassle-free scalability, sidelining complications like database upkeep or node synchronization. In terms of features, KrakenD is a jack-of-all-trades. It accommodates multiple protocols and API standards, offering granular access control, data shaping, and caching capabilities. A standout feature is its Backend For Frontend pattern, which consolidates various API calls into a single response, simplifying client interactions. On the security front, KrakenD is OWASP-compliant and data-agnostic, streamlining regulatory adherence. Operational ease comes via its declarative setup and robust third-party tool integration. With its open-source community edition and transparent pricing model, KrakenD is the go-to API Gateway for organizations that refuse to compromise on performance or scalability.

Tabular is an innovative open table storage solution designed by the same team behind Apache Iceberg, allowing seamless integration with various computing engines and frameworks. By leveraging this technology, users can significantly reduce both query times and storage expenses, achieving savings of up to 50%. It centralizes the enforcement of role-based access control (RBAC) policies, ensuring data security is consistently maintained. The platform is compatible with multiple query engines and frameworks, such as Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python, offering extensive flexibility. With features like intelligent compaction and clustering, as well as other automated data services, Tabular further enhances efficiency by minimizing storage costs and speeding up query performance. It allows for unified data access at various levels, whether at the database or table. Additionally, managing RBAC controls is straightforward, ensuring that security measures are not only consistent but also easily auditable. Tabular excels in usability, providing robust ingestion capabilities and performance, all while maintaining effective RBAC management. Ultimately, it empowers users to select from a variety of top-tier compute engines, each tailored to their specific strengths, while also enabling precise privilege assignments at the database, table, or even column level. This combination of features makes Tabular a powerful tool for modern data management.

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

AWS Lake Formation is a service designed to streamline the creation of a secure data lake in just a matter of days. A data lake serves as a centralized, carefully organized, and protected repository that accommodates all data, maintaining both its raw and processed formats for analytical purposes. By utilizing a data lake, organizations can eliminate data silos and integrate various analytical approaches, leading to deeper insights and more informed business choices. However, the traditional process of establishing and maintaining data lakes is often burdened with labor-intensive, complex, and time-consuming tasks. This includes activities such as importing data from various sources, overseeing data flows, configuring partitions, enabling encryption and managing encryption keys, defining and monitoring transformation jobs, reorganizing data into a columnar structure, removing duplicate records, and linking related entries. After data is successfully loaded into the data lake, it is essential to implement precise access controls for datasets and continuously monitor access across a broad spectrum of analytics and machine learning tools and services. The comprehensive management of these tasks can significantly enhance the overall efficiency and security of data handling within an organization.

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.

Aserto empowers developers to create secure applications effortlessly. It simplifies the integration of detailed, policy-driven, real-time access control into applications and APIs. By managing all the complexities associated with secure, scalable, and high-performance access management, Aserto streamlines the process significantly. The platform provides speedy authorization through a local library alongside a centralized control plane to oversee policies, user attributes, relationship data, and decision logs. It is equipped with the necessary tools to implement both Role-Based Access Control (RBAC) and more nuanced authorization frameworks like Attribute-Based Access Control (ABAC) and Relationship-Based Access Control (ReBAC). You can explore our open-source initiatives, such as Topaz.sh, which serves as a standalone authorizer deployable in your infrastructure, enabling fine-grained access control for your applications. Topaz allows the integration of OPA policies with Zanzibar's data model, offering unparalleled flexibility. Another project, OpenPolicyContainers.com (OPCR), enhances the security of OPA policies throughout their lifecycle by enabling tagging and versioning features. These tools collectively enhance the security and efficiency of application development in today's digital landscape.

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.

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.

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

Experience serverless and interactive data querying with IBM Cloud Object Storage, enabling you to analyze your data directly at its source without the need for ETL processes, databases, or infrastructure management. IBM Cloud SQL Query leverages Apache Spark, a high-performance, open-source data processing engine designed for quick and flexible analysis, allowing SQL queries without requiring ETL or schema definitions. You can easily perform data analysis on your IBM Cloud Object Storage via our intuitive query editor and REST API. With a pay-per-query pricing model, you only incur costs for the data that is scanned, providing a cost-effective solution that allows for unlimited queries. To enhance both savings and performance, consider compressing or partitioning your data. Furthermore, IBM Cloud SQL Query ensures high availability by executing queries across compute resources located in various facilities. Supporting multiple data formats, including CSV, JSON, and Parquet, it also accommodates standard ANSI SQL for your querying needs, making it a versatile tool for data analysis. This capability empowers organizations to make data-driven decisions more efficiently than ever before.

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.

SecuPi presents a comprehensive data-centric security solution that includes advanced fine-grained access control (ABAC), Database Activity Monitoring (DAM), and various de-identification techniques such as FPE encryption, physical and dynamic masking, and right to be forgotten (RTBF) deletion. This platform is designed to provide extensive protection across both commercial and custom applications, encompassing direct access tools, big data environments, and cloud infrastructures. With SecuPi, organizations can utilize a single data security framework to effortlessly monitor, control, encrypt, and categorize their data across all cloud and on-premises systems without requiring any modifications to existing code. The platform is agile and configurable, enabling it to adapt to both current and future regulatory and auditing demands. Additionally, its implementation is rapid and cost-effective, as it does not necessitate any alterations to source code. SecuPi's fine-grained data access controls ensure that sensitive information is safeguarded, granting users access solely to the data they are entitled to, while also integrating smoothly with Starburst/Trino to automate the enforcement of data access policies and enhance data protection efforts. This capability allows organizations to maintain compliance and security effortlessly as they navigate their data management challenges.

Tokern offers an open-source suite designed for data governance, specifically tailored for databases and data lakes. This user-friendly toolkit facilitates the collection, organization, and analysis of metadata from data lakes, allowing users to execute quick tasks via a command-line application or run it as a service for ongoing metadata collection. Users can delve into aspects like data lineage, access controls, and personally identifiable information (PII) datasets, utilizing reporting dashboards or Jupyter notebooks for programmatic analysis. As a comprehensive solution, Tokern aims to enhance your data's return on investment, ensure compliance with regulations such as HIPAA, CCPA, and GDPR, and safeguard sensitive information against insider threats seamlessly. It provides centralized management for metadata related to users, datasets, and jobs, which supports various other data governance functionalities. With the capability to track Column Level Data Lineage for platforms like Snowflake, AWS Redshift, and BigQuery, users can construct lineage from query histories or ETL scripts. Additionally, lineage exploration can be achieved through interactive graphs or programmatically via APIs or SDKs, offering a versatile approach to understanding data flow. Overall, Tokern empowers organizations to maintain robust data governance while navigating complex regulatory landscapes.

Imply is a cutting-edge analytics platform that leverages Apache Druid to manage extensive, high-performance OLAP (Online Analytical Processing) tasks in real-time. It excels at ingesting data instantly, delivering rapid query results, and enabling intricate analytical inquiries across vast datasets while maintaining low latency. This platform is specifically designed for enterprises that require engaging analytics, real-time dashboards, and data-centric decision-making on a large scale. Users benefit from an intuitive interface for exploring data, enhanced by features like multi-tenancy, detailed access controls, and operational insights. Its distributed architecture and ability to scale make Imply particularly advantageous for applications in streaming data analysis, business intelligence, and real-time monitoring across various sectors. Furthermore, its capabilities ensure that organizations can efficiently adapt to increasing data demands and quickly derive actionable insights from their data.

Effortlessly import marketing data into your preferred data warehouse or cloud storage solution, including BigQuery, Snowflake, Redshift, Azure SQL, AWS S3, Azure Data Lake, and Google Cloud Storage, through our fully-managed ETL pipelines hosted in the cloud. Our comprehensive marketing data warehouse consolidates all your marketing information and delivers valuable insights, such as advertising performance, cross-channel attribution, content analysis, competitor intelligence, and much more. Additionally, our customer data platform facilitates real-time identity resolution across various data sources, providing a cohesive view of the customer and their journey. Electrik.AI serves as a cloud-driven marketing analytics software and an all-encompassing service platform designed to optimize your marketing efforts. Moreover, Electrik.AI’s Google Analytics Hit Data Extractor is capable of enhancing and retrieving the un-sampled hit-level data transmitted to Google Analytics from your website or application, routinely transferring it to your specified destination database, data warehouse, or data lake for further analysis. This ensures you have access to the most accurate and actionable data to drive your marketing strategies effectively.

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.

Iceberg is an advanced format designed for managing extensive analytical tables efficiently. It combines the dependability and ease of SQL tables with the capabilities required for big data, enabling multiple engines such as Spark, Trino, Flink, Presto, Hive, and Impala to access and manipulate the same tables concurrently without issues. The format allows for versatile SQL operations to incorporate new data, modify existing records, and execute precise deletions. Additionally, Iceberg can optimize read performance by eagerly rewriting data files or utilize delete deltas to facilitate quicker updates. It also streamlines the complex and often error-prone process of generating partition values for table rows while automatically bypassing unnecessary partitions and files. Fast queries do not require extra filtering, and the structure of the table can be adjusted dynamically as data and query patterns evolve, ensuring efficiency and adaptability in data management. This adaptability makes Iceberg an essential tool in modern data workflows.

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.

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

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

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

Amazon EMR stands as the leading cloud-based big data solution for handling extensive datasets through popular open-source frameworks like Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. This platform enables you to conduct Petabyte-scale analyses at a cost that is less than half of traditional on-premises systems and delivers performance more than three times faster than typical Apache Spark operations. For short-duration tasks, you have the flexibility to quickly launch and terminate clusters, incurring charges only for the seconds the instances are active. In contrast, for extended workloads, you can establish highly available clusters that automatically adapt to fluctuating demand. Additionally, if you already utilize open-source technologies like Apache Spark and Apache Hive on-premises, you can seamlessly operate EMR clusters on AWS Outposts. Furthermore, you can leverage open-source machine learning libraries such as Apache Spark MLlib, TensorFlow, and Apache MXNet for data analysis. Integrating with Amazon SageMaker Studio allows for efficient large-scale model training, comprehensive analysis, and detailed reporting, enhancing your data processing capabilities even further. This robust infrastructure is ideal for organizations seeking to maximize efficiency while minimizing costs in their data operations.

SelectDB is an innovative data warehouse built on Apache Doris, designed for swift query analysis on extensive real-time datasets. Transitioning from Clickhouse to Apache Doris facilitates the separation of the data lake and promotes an upgrade to a more efficient lake warehouse structure. This high-speed OLAP system handles nearly a billion query requests daily, catering to various data service needs across multiple scenarios. To address issues such as storage redundancy, resource contention, and the complexities of data governance and querying, the original lake warehouse architecture was restructured with Apache Doris. By leveraging Doris's capabilities for materialized view rewriting and automated services, it achieves both high-performance data querying and adaptable data governance strategies. The system allows for real-time data writing within seconds and enables the synchronization of streaming data from databases. With a storage engine that supports immediate updates and enhancements, it also facilitates real-time pre-polymerization of data for improved processing efficiency. This integration marks a significant advancement in the management and utilization of large-scale real-time data.

OpenDocMan is an open-source document management system (DMS) that is web-based and written in PHP, crafted to meet ISO 17025 and OIE standards for managing documents. It offers online access, detailed control over file access, and simplifies installation and upgrades through automation. Available under the open-source GPL license, this system permits free use and modifications. User feedback is highly valued, and suggestions or reports of issues are encouraged to enhance the software. Utilizing free document management software can greatly benefit organizations, as IT personnel and managers have the ability to assign document management responsibilities to various staff members by configuring user and group permissions. These permissions can be tailored to be either strict or lenient based on the organization's needs, thus providing flexibility in managing access.

CMQ is capable of handling the transmission and reception of tens of millions of messages while also allowing for the retention of an infinite number of messages. With its remarkable throughput, it can manage over 100,000 queries per second (QPS) within a single cluster, fully catering to the messaging requirements of your enterprise. In the event that a message is returned to the user, CMQ ensures reliability by creating three distinct copies of the message data across various physical servers, allowing for swift data migration through its backend replication system in the case of server failure. Furthermore, CMQ provides secure access via HTTPS and leverages Tencent Cloud's comprehensive security measures to safeguard against network threats and ensure the confidentiality of your business information. Additionally, it facilitates the management of master/sub-accounts and collaborator accounts, allowing for precise control over resource access, which enhances overall security and operational efficiency. With such robust features, CMQ proves to be an indispensable tool for businesses looking to optimize their messaging infrastructure.

The system is adaptable, featuring a programming model that facilitates the effective generation of machine code, offering precise control over both dynamic and static functionalities. It outlines the Open Dylan implementation of the Dylan programming language, including a fundamental set of Dylan libraries and a mechanism for library interchange. These core libraries encompass various language enhancements, a threading interface, and modules for object finalization, as well as printing and output formatting. Additionally, it includes a streams module, a sockets module, and components that interface with operating system functionalities like file system management, time and date handling, and the host machine's environment, along with a foreign function interface and access to certain low-level aspects of the Microsoft Win32 API. This comprehensive structure allows developers to create robust applications while leveraging existing system capabilities.

Effortlessly capture, modify, and transfer streaming data in real time. You can create a delivery stream, choose your desired destination, and begin streaming data with minimal effort. The system automatically provisions and scales necessary compute, memory, and network resources without the need for continuous management. You can convert raw streaming data into various formats such as Apache Parquet and dynamically partition it without the hassle of developing your processing pipelines. Amazon Data Firehose is the most straightforward method to obtain, transform, and dispatch data streams in mere seconds to data lakes, data warehouses, and analytics platforms. To utilize Amazon Data Firehose, simply establish a stream by specifying the source, destination, and any transformations needed. The service continuously processes your data stream, automatically adjusts its scale according to the data volume, and ensures delivery within seconds. You can either choose a source for your data stream or utilize the Firehose Direct PUT API to write data directly. This streamlined approach allows for greater efficiency and flexibility in handling data streams.

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.

Apache Ranger™ serves as a framework designed to facilitate, oversee, and manage extensive data security within the Hadoop ecosystem. The goal of Ranger is to implement a thorough security solution throughout the Apache Hadoop landscape. With the introduction of Apache YARN, the Hadoop platform can effectively accommodate a genuine data lake architecture, allowing businesses to operate various workloads in a multi-tenant setting. As the need for data security in Hadoop evolves, it must adapt to cater to diverse use cases regarding data access, while also offering a centralized framework for the administration of security policies and the oversight of user access. This centralized security management allows for the execution of all security-related tasks via a unified user interface or through REST APIs. Additionally, Ranger provides fine-grained authorization, enabling specific actions or operations with any Hadoop component or tool managed through a central administration tool. It standardizes authorization methods across all Hadoop components and enhances support for various authorization strategies, including role-based access control, thereby ensuring a robust security framework. By doing so, it significantly strengthens the overall security posture of organizations leveraging Hadoop technologies.

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.

Permify is an advanced authorization service tailored for developers looking to create and oversee detailed, scalable access control systems within their software applications. Drawing inspiration from Google's Zanzibar, it allows users to organize authorization models, store authorization data in chosen databases, and utilize its API for managing authorization queries across diverse applications and services. The service accommodates various access control models, such as Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), which support the development of detailed permissions and policies. By centralizing authorization logic, Permify abstracts it from the core codebase, making it simpler to reason about, test, and debug. Additionally, it offers a range of flexible policy storage options and includes a role manager for managing RBAC role hierarchies effectively. The platform enhances efficiency in large, multi-tenant setups by implementing filtered policy management, ensuring that access controls are enforced seamlessly across different environments. With its robust features, Permify stands out as a comprehensive solution for modern access management challenges.

Y42 is the first fully managed Modern DataOps Cloud for production-ready data pipelines on top of Google BigQuery and Snowflake.

Orchestrating actions enables the creation of intricate backend agents that can perform multiple tasks seamlessly. Compatible with all LLMs, you can design a completely tailored user interface for your agent without needing to code, all hosted on your own domain. Monitor each phase of your agent’s process, capturing every detail to manage the unpredictable behavior of LLMs effectively. Implement precise access controls for your application, data, and the agent itself. Utilize a specially fine-tuned model designed to expedite the software development process significantly. Additionally, the system automatically manages aspects like concurrency, rate limiting, and various other functionalities to enhance performance and reliability. This comprehensive approach ensures that users can focus on their core objectives while the underlying complexities are handled efficiently.

Experience the freedom of storage that allows data to flow freely without restrictions. With a managed data lake, you can quickly set up your system and start utilizing data without needing to be an expert in the field. Cribl Lake ensures you won’t be overwhelmed by data, enabling effortless storage, management, policy enforcement, and accessibility whenever necessary. Embrace the future with open formats while benefiting from consistent retention, security, and access control policies. Let Cribl take care of the complex tasks, transforming data into a resource that delivers value to your teams and tools. With Cribl Lake, you can be operational in minutes instead of months, thanks to seamless automated provisioning and ready-to-use integrations. Enhance your workflows using Stream and Edge for robust data ingestion and routing capabilities. Cribl Search simplifies your querying process, providing a unified approach regardless of where your data resides, so you can extract insights without unnecessary delays. Follow a straightforward route to gather and maintain data for the long haul while easily meeting legal and business obligations for data retention by setting specific retention timelines. By prioritizing user-friendliness and efficiency, Cribl Lake equips you with the tools needed to maximize data utility and compliance.

Alibaba Cloud's Photo and Drive Service (PDS) allows users to create a robust cloud storage solution tailored for customers, featuring enterprise-grade capabilities including extensive file storage, rapid file sharing, comprehensive file and directory management, precise access and permission controls, along with advanced AI-driven file analysis and categorization. Experience exceptional speed when managing files, as Alibaba Cloud Drive utilizes centralized metadata storage and a globally accelerated network for swift uploading, sharing, and downloading. Leverage Alibaba Cloud’s AI technology to extract, identify, and reorganize file metadata, accommodating extensive data queries and enhancing the understanding of unstructured information. Safeguard your data with robust security measures, including server-side encryption, HTTPS 2.0 transmission protocols, thorough data validation processes, versatile authorization options, and effective file watermarking features to maintain integrity and privacy. With these advanced features, users can ensure a seamless and secure file management experience.

While generative AI is a relatively recent development, our efforts over the last five years have paved the way for this moment. Deep Lake merges the strengths of data lakes and vector databases to craft and enhance enterprise-level solutions powered by large language models, allowing for continual refinement. However, vector search alone does not address retrieval challenges; a serverless query system is necessary for handling multi-modal data that includes embeddings and metadata. You can perform filtering, searching, and much more from either the cloud or your local machine. This platform enables you to visualize and comprehend your data alongside its embeddings, while also allowing you to monitor and compare different versions over time to enhance both your dataset and model. Successful enterprises are not solely reliant on OpenAI APIs, as it is essential to fine-tune your large language models using your own data. Streamlining data efficiently from remote storage to GPUs during model training is crucial. Additionally, Deep Lake datasets can be visualized directly in your web browser or within a Jupyter Notebook interface. You can quickly access various versions of your data, create new datasets through on-the-fly queries, and seamlessly stream them into frameworks like PyTorch or TensorFlow, thus enriching your data processing capabilities. This ensures that users have the flexibility and tools needed to optimize their AI-driven projects effectively.

Amazon Managed Streaming for Apache Kafka (Amazon MSK) simplifies the process of creating and operating applications that leverage Apache Kafka for handling streaming data. As an open-source framework, Apache Kafka enables the construction of real-time data pipelines and applications. Utilizing Amazon MSK allows you to harness the native APIs of Apache Kafka for various tasks, such as populating data lakes, facilitating data exchange between databases, and fueling machine learning and analytical solutions. However, managing Apache Kafka clusters independently can be quite complex, requiring tasks like server provisioning, manual configuration, and handling server failures. Additionally, you must orchestrate updates and patches, design the cluster to ensure high availability, secure and durably store data, establish monitoring systems, and strategically plan for scaling to accommodate fluctuating workloads. By utilizing Amazon MSK, you can alleviate many of these burdens and focus more on developing your applications rather than managing the underlying infrastructure.

You can now discover a platform for rapid application development that will accelerate your digital initiatives without vendor dependence, low-code limitations, or usage-based fees. Jmix is a general purpose open architecture that uses a future-proof technology stack and can support multiple digital initiatives throughout the organization. Jmix applications are yours and can be used independently by using an open-source runtime that uses mainstream technologies. With a server-side frontend model and fine-grained access controls, your data is protected. Java and Kotlin developers can be considered full-stack Jmix developers. You don't need separate frontend or backend teams. Visual tools are useful for developers who are new to the platform or have not had much experience. Jmix's data-centric approach makes it easy to migrate legacy applications. Jmix boosts productivity and provides ready-to-use components to help you get the job done.

A comprehensive open-source session replay suite designed specifically for developers, enabling self-hosting for optimal data control. Gain insights into every issue as if it were occurring in your own browser, allowing you to delve deep while observing user interactions. This all-in-one platform empowers developers to effectively address problems, replay sessions, track web app performance, and provide exceptional customer support. Experience your users' journey firsthand, identify their challenges, expose hidden issues, and create outstanding experiences. With the ability to self-host, your customer data remains within your own infrastructure, eliminating the need to share sensitive information with third parties. Maintain complete authority over the data captured and eliminate the hassle of extensive compliance and security protocols. Our suite also includes advanced privacy features for user data sanitization. If self-deployment isn't your preference, you can easily opt for our cloud solution and start benefiting from the service in just a few minutes. This flexibility ensures that developers can choose the deployment method that best suits their needs while still prioritizing user privacy and data security.

Amazon DataZone serves as a comprehensive data management solution that empowers users to catalog, explore, share, and regulate data from various sources, including AWS, on-premises systems, and third-party platforms. It provides administrators and data stewards with the ability to manage and oversee data access with precision, guaranteeing that users possess the correct level of permissions and contextual understanding. This service streamlines data access for a diverse range of professionals, such as engineers, data scientists, product managers, analysts, and business users, thereby promoting insights driven by data through enhanced collaboration. Among its notable features are a business data catalog that enables searching and requesting access to published datasets, tools for project collaboration to oversee and manage data assets, a user-friendly web portal offering tailored views for data analysis, and regulated data sharing workflows that ensure proper access. Furthermore, Amazon DataZone leverages machine learning to automate the processes of data discovery and cataloging, making it an invaluable resource for organizations striving to maximize their data utility. As a result, it significantly enhances the efficiency of data governance and utilization across various business functions.

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.

TruLens is a versatile open-source Python library aimed at the systematic evaluation and monitoring of Large Language Model (LLM) applications. It features detailed instrumentation, feedback mechanisms, and an intuitive interface that allows developers to compare and refine various versions of their applications, thereby promoting swift enhancements in LLM-driven projects. The library includes programmatic tools that evaluate the quality of inputs, outputs, and intermediate results, enabling efficient and scalable assessments. With its precise, stack-agnostic instrumentation and thorough evaluations, TruLens assists in pinpointing failure modes while fostering systematic improvements in applications. Developers benefit from an accessible interface that aids in comparing different application versions, supporting informed decision-making and optimization strategies. TruLens caters to a wide range of applications, including but not limited to question-answering, summarization, retrieval-augmented generation, and agent-based systems, making it a valuable asset for diverse development needs. As developers leverage TruLens, they can expect to achieve more reliable and effective LLM applications.

Impala offers rapid response times and accommodates numerous concurrent users for business intelligence and analytical inquiries within the Hadoop ecosystem, supporting technologies such as Iceberg, various open data formats, and multiple cloud storage solutions. Additionally, it exhibits linear scalability, even when deployed in environments with multiple tenants. The platform seamlessly integrates with Hadoop's native security measures and employs Kerberos for user authentication, while the Ranger module provides a means to manage permissions, ensuring that only authorized users and applications can access specific data. You can leverage the same file formats, data types, metadata, and frameworks for security and resource management as those used in your Hadoop setup, avoiding unnecessary infrastructure and preventing data duplication or conversion. For users familiar with Apache Hive, Impala is compatible with the same metadata and ODBC driver, streamlining the transition. It also supports SQL, which eliminates the need to develop a new implementation from scratch. With Impala, a greater number of users can access and analyze a wider array of data through a unified repository, relying on metadata that tracks information right from the source to analysis. This unified approach enhances efficiency and optimizes data accessibility across various applications.

Oracle Cloud Infrastructure (OCI) Functions provides a serverless computing platform that allows developers to design, execute, and scale applications without the burden of managing the underlying infrastructure. This service is based on the open-source Fn Project and accommodates various programming languages such as Python, Go, Java, Node.js, and C#, which facilitates versatile function development. Developers can easily deploy their code, as OCI takes care of the automatic provisioning and scaling of resources needed for execution. Additionally, it features provisioned concurrency, which guarantees that functions are ready to handle requests with minimal delay. A rich catalog of prebuilt functions is offered, allowing users to quickly implement standard tasks without starting from scratch. Functions are bundled as Docker images, and experienced developers have the option to create custom runtime environments using Dockerfiles. Furthermore, integration with Oracle Identity and Access Management allows for precise control over access permissions, while OCI Vault ensures that sensitive configuration data is stored securely. Overall, this combination of features makes OCI Functions a powerful tool for modern application development.

Alluxio stands out as the pioneering open-source technology for data orchestration tailored for analytics and AI within cloud environments. It effectively connects data-centric applications with various storage systems, allowing seamless data retrieval from the storage layer, thus enhancing accessibility and enabling a unified interface for multiple storage solutions. The innovative memory-first tiered architecture of Alluxio facilitates data access at unprecedented speeds, significantly surpassing traditional methods. Picture yourself as an IT leader with the power to select from a diverse range of services available in both public cloud and on-premises settings. Furthermore, envision having the capability to scale your storage for data lakes while maintaining control over data locality and ensuring robust protection for your organization. To support these aspirations, NetApp and Alluxio are collaborating to empower clients in navigating the evolving landscape of modernizing their data architecture, with an emphasis on minimizing operational complexity for analytics, machine learning, and AI-driven workflows. This partnership aims to unlock new possibilities for businesses striving to harness the full potential of their data assets.

Red Hat® Quay is a container image registry that facilitates the storage, creation, distribution, and deployment of containers. It enhances the security of your image repositories through automation, authentication, and authorization mechanisms. Quay can be utilized within OpenShift or as an independent solution. You can manage access to the registry using a variety of identity and authentication providers, which also allows for team and organization mapping. A detailed permissions system aligns with your organizational hierarchy, ensuring appropriate access levels. Transport layer security encryption ensures secure communication between Quay.io and your servers automatically. Additionally, integrate vulnerability detection tools, such as Clair, to perform automatic scans of your container images, and receive notifications regarding any identified vulnerabilities. This setup helps optimize your continuous integration and continuous delivery (CI/CD) pipeline by utilizing build triggers, git hooks, and robot accounts. For further transparency, you can audit your CI pipeline by monitoring both API and user interface actions, thereby maintaining oversight of operations. In this way, Quay not only secures your container images but also streamlines your development processes.

The ARCON | Endpoint Privilege Management solution (EPM) provides endpoint privileges in a ‘just-in-time’ or ‘on-demand’ manner while overseeing all end users on your behalf. This tool is adept at identifying insider threats, compromised identities, and various malicious attempts to infiltrate endpoints. Equipped with a robust User Behavior Analytics component, it monitors typical behaviors of end users, thereby recognizing unusual behavior patterns and other entities within the network. A unified governance framework allows you to blacklist harmful applications, restrict data transfers from devices to removable storage, and offers meticulous control over application access with the capability for ‘just-in-time’ privilege elevation and demotion. Regardless of the number of endpoints resulting from remote work and access, you can secure them all with this singular endpoint management solution. Enjoy the flexibility of elevating privileges at your discretion, whenever it suits you. Plus, the ease of managing all these features through one platform enhances the overall security experience significantly.

Apache Spark™ serves as a comprehensive analytics platform designed for large-scale data processing. It delivers exceptional performance for both batch and streaming data by employing an advanced Directed Acyclic Graph (DAG) scheduler, a sophisticated query optimizer, and a robust execution engine. With over 80 high-level operators available, Spark simplifies the development of parallel applications. Additionally, it supports interactive use through various shells including Scala, Python, R, and SQL. Spark supports a rich ecosystem of libraries such as SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, allowing for seamless integration within a single application. It is compatible with various environments, including Hadoop, Apache Mesos, Kubernetes, and standalone setups, as well as cloud deployments. Furthermore, Spark can connect to a multitude of data sources, enabling access to data stored in systems like HDFS, Alluxio, Apache Cassandra, Apache HBase, and Apache Hive, among many others. This versatility makes Spark an invaluable tool for organizations looking to harness the power of large-scale data analytics.

Amazon Security Lake seamlessly consolidates security information from various AWS environments, SaaS platforms, on-premises systems, and cloud sources into a specialized data lake within your account. This service enables you to gain a comprehensive insight into your security data across the entire organization, enhancing the safeguarding of your workloads, applications, and data. By utilizing the Open Cybersecurity Schema Framework (OCSF), which is an open standard, Security Lake effectively normalizes and integrates security data from AWS along with a wide array of enterprise security data sources. You have the flexibility to use your preferred analytics tools to examine your security data while maintaining full control and ownership over it. Furthermore, you can centralize visibility into data from both cloud and on-premises sources across your AWS accounts and Regions. This approach not only streamlines your data management at scale but also ensures consistency in your security data by adhering to an open standard, allowing for more efficient and effective security practices across your organization. Ultimately, this solution empowers organizations to respond to security threats more swiftly and intelligently.

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.