Alternatives to BigLake

ANSI SQL allows you to analyze petabytes worth of data at lightning-fast speeds with no operational overhead. Analytics at scale with 26%-34% less three-year TCO than cloud-based data warehouse alternatives. You can unleash your insights with a trusted platform that is more secure and scales with you. Multi-cloud analytics solutions that allow you to gain insights from all types of data. You can query streaming data in real-time and get the most current information about all your business processes. Machine learning is built-in and allows you to predict business outcomes quickly without having to move data. With just a few clicks, you can securely access and share the analytical insights within your organization. Easy creation of stunning dashboards and reports using popular business intelligence tools right out of the box. BigQuery's strong security, governance, and reliability controls ensure high availability and a 99.9% uptime SLA. Encrypt your data by default and with customer-managed encryption keys

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

Amazon Redshift is preferred by more customers than any other cloud data storage. Redshift powers analytic workloads for Fortune 500 companies and startups, as well as everything in between. Redshift has helped Lyft grow from a startup to multi-billion-dollar enterprises. It's easier than any other data warehouse to gain new insights from all of your data. Redshift allows you to query petabytes (or more) of structured and semi-structured information across your operational database, data warehouse, and data lake using standard SQL. Redshift allows you to save your queries to your S3 database using open formats such as Apache Parquet. This allows you to further analyze other analytics services like Amazon EMR and Amazon Athena. Redshift is the fastest cloud data warehouse in the world and it gets faster each year. The new RA3 instances can be used for performance-intensive workloads to achieve up to 3x the performance compared to any cloud data warehouse.

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

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

We make it simple for developers to secure their cloud apps. Adapt your authorization model so that it supports the principle of least privilige with fine-grained accessibility. Authorization decisions are based on the users, groups, domain models, resource hierarchy and relationships between them. Locally make authorization decisions using real-time information in milliseconds with 100% availability. Locally enforce using real-time information. Manage policies from one location. Define and manage all policies for your applications from a central location. Spend less time on access control and more time delivering core features. Allowing policy and code to develop independently will streamline the interaction between engineering and security. Create a secure supply chain for software that supports your policies. Store and version code for your policies in a git repository, just like you would any other code. Just like any other application artifact, you can build, tag, sign and immutable images of your policies.

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

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

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

SecuPi is a data-centric platform that provides a comprehensive security solution. It offers fine-grained control of access (ABAC), Database Activity Monitor (DAM), and de-identification through FPE encryption and masking, both physical and dynamic (RTBF). SecuPi covers a wide range of applications including packaged and home-grown, direct access tools, cloud environments, big data and cloud environments, as well as packaged and homegrown applications. One data security platform to monitor, control, encrypt and classify data across cloud & on-prem without code changes. Platform that is agile and configurable to meet current and future audit and regulatory requirements. Implementation is fast and cost-effective with no source-code changes. SecuPi’s fine-grain controls for data access protect sensitive data, so that users only see the data they are allowed to view. Seamlessly integrates with Starburst/Trino to automate data access policies and protection operations.

Interactive querying that is serverless for analyzing data stored in IBM Cloud Object Storage. You can query your data right where it is stored - there are no ETL, databases or infrastructure to manage.

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

Open source data governance suite to manage data lakes and databases. Tokern is an easy-to-use toolkit for collecting, organizing and analysing metadata from data lakes. Runs as a command-line application for quick tasks. Run as a service to continuously collect metadata. Use reporting dashboards to analyze lineage, access control, and PII data. Or programmatically in Jupyter notebooks. Tokern is an open-source data governance suite for data lakes and databases. You can improve the ROI of your data, comply to regulations like HIPAA, CCPA, and GDPR, and protect your data from insider threats with confidence. Centralized metadata management for users, jobs, and datasets. Other data governance features are powered by this feature. Track column-level data lineage for Snowflake and AWS Redshift. You can build lineage using query history or ETL scripts. Interactive graphs and programming with APIs and SDKs allow you to explore lineage.

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

Imply is a real time analytics platform built on Apache Druid. It was designed to handle large scale, high performance OLAP (Online Analytical Processing). It provides real-time data ingestion and fast query performance. It also allows for complex analytical queries to be performed on massive datasets at low latency. Imply is designed for organizations who need interactive analytics, real time dashboards, and data driven decision-making. It offers a user-friendly data exploration interface, as well as advanced features like multi-tenancy and fine-grained controls for access. Imply's distributed architecture and scalability make it ideal for use cases such as streaming data analytics, real-time monitoring, and business intelligence.

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.

Iceberg is an efficient format for large analytical tables. Iceberg brings the simplicity and reliability of SQL tables to the world of big data. It also allows engines like Spark, Trino Flink Presto Hive Impala and Impala to work safely with the same tables at the same time. Iceberg supports SQL commands that are flexible to merge new data, update rows, and perform targeted deletions. Iceberg can eagerly write data files to improve read performance or it can use delete-deltas for faster updates. Iceberg automates the tedious, error-prone process of generating partition values for each row in a table. It also skips unnecessary files and partitions. There are no extra filters needed for fast queries and the table layout is easily updated when data or queries change.

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

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

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

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

Amazon EMR is the market-leading cloud big data platform. It processes large amounts of data with open source tools like Apache Spark, Apache Hive and Apache HBase. EMR allows you to run petabyte-scale analysis at a fraction of the cost of traditional on premises solutions. It is also 3x faster than standard Apache Spark. You can spin up and down clusters for short-running jobs and only pay per second for the instances. You can also create highly available clusters that scale automatically to meet the demand for long-running workloads. You can also run EMR clusters from AWS Outposts if you have on-premises open source tools like Apache Spark or Apache Hive.

CMQ can send/receive, push and push tens to millions of messages efficiently and can retain an unlimited amount of messages. It can process more than 100,000 queries per second (QPS), with one cluster. This allows it to fully meet your business' messaging needs. CMQ creates three copies of each message to be returned to the user. This allows the backend data replication mechanism to quickly migrate data to other servers in case one fails. CMQ supports HTTPS secure access and Tencent Cloud's multidimensional security protection to protect your business from network attacks. It also supports the management of master/sub-accounts as well as collaborator accounts, which allows for fine-grained access control to resource access.

It is dynamic, while offering a programming model that supports efficient machine code generation. This includes fine-grained control of dynamic and static behaviors. This document describes the Open Dylan implementation, including a core set Dylan libraries and a library interchange mechanism. The core libraries include many language extensions, a threads interface and object finalization and printing and output formatting module. There are also modules that provide an interface to operating system features like the file system, time, date information, and foreign function interfaces.

OpenDocMan, a web-based, free and open-source document management software (DMS), was written in PHP. It conforms to the ISO 17025 standard for document management and OIE standards. It offers web-based access, fine-grained access control, automated installation and upgrades, and web-based access. OpenDocMan was created under the GPL open-source license. This basically allows you to use the program at no cost and modify it in any way you like. If you have any questions or concerns, we welcome feedback. You get free document management software. IT managers and IT staff can delegate document management tasks to any number of staff members through user and group permissions. You can set permissions as restrictively as you like or as permissive as necessary.

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™, a framework that enables, monitors and manages comprehensive data security across Hadoop's platform, is called Apache Ranger. Ranger's goal is to provide complete security across the Apache Hadoop ecosystem. Apache YARN has made it possible to create a data lake architecture on Hadoop. Multi-tenant environments allow enterprises to run multiple workloads. Hadoop data security must evolve to support multiple use-cases for data access. It also provides a framework for central administration and monitoring of user access. All security-related tasks can be managed centrally through a UI or REST APIs using central security administration. Fine-grained authorization to perform a specific action or operation with a Hadoop component/tool. This is managed through a central admin tool. Standardize authorization methods across all Hadoop components. Enhanced support for different authorization methods, such as Role-based access control, etc.

Alibaba Cloud Photo and Drive Service allows you to create a cloud drive and offer it to your clients with enterprise-level features such as large-volume storage, ultrafast file sharing, directory management, finely-grained permission control and access, and AI file classification and analysis. Alibaba Cloud Drive's global accelerated network and centralized metadata storage allows you to store, share, and download files at super-fast speeds. Alibaba Cloud's AI capabilities can be used to extract, recognize, and reclassify file metadata, as well as support massive data queries. Data security is ensured with server-side encryption, HTTPS 2.0 transmission, end to end data validation, flexible authorisation methods, and file-watermarking functions.

Build complex backend agents using multiple steps with an action-based orchestration. All LLMs are supported. Build a fully customized UI without writing a line of code for your agent, and serve it on your own domain. Track your agent's every move, literally, to cope with the nondeterministic nature LLMs. Access control can be built at a finer grain for your application, data and agent. A fine-tuned, specialized model to accelerate software development. Automatically handle concurrency and rate limiting.

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

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

Open-source session replay suite for developers. Self-hosted for full data control. Every issue can be understood as if it were happening in your browser. While watching your users, look under the hood. Everything developers need to fix what's wrong. One platform to replay sessions and understand issues, monitor your website, and help your customers. Feel the pain of your users. Feel their pain, discover hidden issues, and create amazing experiences. You can host a full-featured replay session, so that your customer data does not leave your infrastructure. No more sharing your data with third parties. You have complete control over what data is captured. Don't waste time on lengthy compliance or security checks. For sanitizing user data, fine-grained privacy features are available. You can host your session replay tool and stop sending data out to third parties. Are you not a fan of self-deployments Get started quickly with our cloud

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

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

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.

TruLens, an open-source Python Library, is designed to evaluate and track Large Language Model applications. It offers fine-grained instruments, feedback functions and a user-interface to compare and iterate app versions. This facilitates rapid development and improvement of LLM based applications. Tools that allow scalable evaluation of the inputs, outputs and intermediate results of LLM applications. Instrumentation that is fine-grained and stack-agnostic, and comprehensive evaluations can help identify failure modes. A simple interface allows developers to compare versions of their application, facilitating informed decisions and optimization. TruLens supports a variety of use cases, such as question-answering and summarization. It also supports retrieval-augmented generation and agent-based apps.

Doolytic is a leader in big data discovery, the convergence data discovery, advanced analytics and big data. Doolytic is bringing together BI experts to revolutionize self-service exploration of large data. This will unleash the data scientist in everyone. doolytic is an enterprise solution for native big data discovery. doolytic is built on open-source, scalable technologies that are best-of-breed. Lightening performance on billions and petabytes. Structured, unstructured, and real-time data from all sources. Advanced query capabilities for experts, Integration with R to enable advanced and predictive applications. With Elastic's flexibility, you can search, analyze, and visualize data in real-time from any format or source. You can harness the power of Hadoop data lakes without any latency or concurrency issues. doolytic solves common BI issues and enables big data discovery without clumsy or inefficient workarounds.

Red Hat® Quay container registry provides storage that allows you to build, distribute and deploy containers. Automated authentication, authorization, and authorization systems give you more control over your image repositories. Quay can be used with OpenShift as a standalone component or as an extension to OpenShift. Multiple identity and authentication providers can be used to control access to the registry, including support for organizations and teams. To map to your organization structure, use a fine-grained permissions scheme. Transport layer security encryption allows you to transit between Quay.io servers and Quay.io. Integrate with vulnerability detectors like Clair to automatically scan container images. Notifications will alert you to known vulnerabilities. Streamline your continuous integration/continuous delivery (CI/CD) pipeline with build triggers, git hooks, and robot accounts. Track API and UI actions to audit your CI pipeline.

Amazon MSK is a fully managed service that makes coding and running applications that use Apache Kafka for streaming data processing easy. Apache Kafka is an open source platform that allows you to build real-time streaming data applications and pipelines. Amazon MSK allows you to use native Apache Kafka APIs for populating data lakes, stream changes between databases, and to power machine learning or analytics applications. It is difficult to set up, scale, and manage Apache Kafka clusters in production. Apache Kafka clusters can be difficult to set up and scale on your own.

Impala offers low latency, high concurrency, and a wide range of storage options, including Iceberg and open data formats. Impala scales linearly in multitenant environments. Impala integrates native Hadoop security, Kerberos authentication, and the Ranger module to ensure that the correct users and applications have access to the right data. Utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment, with no redundant infrastructure or data conversion/duplication. Impala uses the same metadata driver and ODBC driver as Apache Hive. Impala, like Hive, supports SQL. You don't need to reinvent the wheel. Impala allows more users to interact with data, whether they are using SQL queries or BI apps, through a single repository. Metadata is also stored from the source of the data until it has been analyzed.

Endpoint Privilege Management solution (EPM) grants endpoint privileges 'just-in-time' or 'on-demand' and monitors all end users for you. This tool detects insider threats, compromised identity, and other malicious attempts at breaching endpoints. It also includes a powerful User Behavior Analytics component that records the normal behavior of end users and helps identify atypical behavior profiles as well as other entities in the network. You can blacklist malicious apps, prevent data from being copied to removable storage devices, and have fine-grained access all applications with 'just in-time' privilege elevation or demotion capabilities. Secure all your endpoints with one endpoint management tool, regardless of how many they may have due to WFH or remote access workplaces. You can elevate privileges at your own discretion and at your convenience.

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

Alluxio is the first open-source data orchestration technology for cloud analytics and AI. It bridges the gap between storage systems and data driven applications, bringing data from the storage layer closer to the data driven apps and making it easy to access. This allows applications to connect to multiple storage systems via a common interface. Alluxio's memory first tiered architecture allows data access at speeds orders-of-magnitude faster than other solutions.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.

Starburst allows you to make better decisions by having quick access to all of your data. Your company has more data than ever, but your data teams are still waiting to analyze it. Starburst gives your data teams quick and accurate access to more data. Starburst Enterprise, a fully supported, production-tested, enterprise-grade distribution for open source Trino (formerly Presto®, SQL), is now available. It increases performance and security, while making it easy for you to deploy, connect, manage, and manage your Trino environment. Starburst allows your team to connect to any source of data, whether it's on-premise, in a cloud, or across a hybrid cloud environment. This allows them to use the analytics tools they already love and access data that lives anywhere.

GeoSpock DB - The space-time analytics database - allows data fusion in the connected world. GeoSpockDB is a unique cloud-native database that can be used to query for real-world applications. It can combine multiple sources of Internet of Things data to unlock their full potential, while simultaneously reducing complexity, cost, and complexity. GeoSpock DB enables data fusion and efficient storage. It also allows you to run ANSI SQL query and connect to analytics tools using JDBC/ODBC connectors. Users can perform analysis and share insights with familiar toolsets. This includes support for common BI tools such as Tableau™, Amazon QuickSight™, and Microsoft Power BI™, as well as Data Science and Machine Learning environments (including Python Notebooks or Apache Spark). The database can be integrated with internal applications as well as web services, including compatibility with open-source visualisation libraries like Cesium.js and Kepler.

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

Manage the entire lifecycle of LLM applications development, testing, deployment and operation without having to piece together multiple systems. Achieving the lowest Total Cost of Ownership (TCO). Featuring a vector database and search engines that outperform all other leading vendors, with 10X less query latency, a 5X higher query rate, and a 3X lower cost. A data and knowledge base that manages large, multi-modal unstructured and structed data efficiently. Never worry about outdated data. Plug and play the latest, advanced, modular, agentic, RAG and GraphRAG without writing plumbing code. You can confidently configure your AI applications using CI/CD evaluations without worrying about regressions. Accelerate iterations to move from development to production in days instead of months. Access control based on roles and privileges.

IBM Analytics for Apache Spark allows data scientists to ask more difficult questions and deliver business value quicker with a flexible, integrated Spark service. It's a simple-to-use, managed service that is always on and doesn't require any long-term commitment. You can start exploring immediately. You can access the power of Apache Spark without locking yourself in, thanks to IBM's open-source commitment as well as decades of enterprise experience. With Notebooks as a connector, coding and analytics are faster and easier with managed Spark services. This allows you to spend more time on innovation and delivery. You can access the power of machine learning libraries through managed Apache Spark services without having to manage a Sparkcluster by yourself.