IBM Db2 Big SQL Alternatives

Google Cloud is a cloud-based service that allows you to create anything from simple websites to complex applications for businesses of all sizes. New customers get $300 in free credits to run, test, and deploy workloads. All customers can use 25+ products for free, up to monthly usage limits. Use Google's core infrastructure, data analytics & machine learning. Secure and fully featured for all enterprises. Tap into big data to find answers faster and build better products. Grow from prototype to production to planet-scale, without having to think about capacity, reliability or performance. From virtual machines with proven price/performance advantages to a fully managed app development platform. Scalable, resilient, high performance object storage and databases for your applications. State-of-the-art software-defined networking products on Google’s private fiber network. Fully managed data warehousing, batch and stream processing, data exploration, Hadoop/Spark, and messaging.

BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data so you can focus on getting valuable business insights quickly. At the core of Google’s data cloud, BigQuery allows you to simplify data integration, cost effectively and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make your organization’s operations more data-driven.

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. • Gain critical real-time insights to run your business • Seamlessly integrate data streaming and batch data • High performance in throughput and low-latency at petabyte scale • Fully-managed cloud service • Tiered storage to optimize cloud performance & spend • Fully-secure & enterprise-ready

Trino is a query engine that runs at ludicrous speed. Fast-distributed SQL query engine for big data analytics that helps you explore your data universe. Trino is a highly parallel and distributed query engine, that is built from the ground up for efficient, low-latency analytics. The largest organizations in the world use Trino to query exabyte-scale data lakes and massive data warehouses alike. Supports diverse use cases, ad-hoc analytics at interactive speeds, massive multi-hour batch queries, and high-volume apps that perform sub-second queries. Trino is an ANSI SQL-compliant query engine, that works with BI tools such as R, Tableau, Power BI, Superset, and many others. You can natively query data in Hadoop, S3, Cassandra, MySQL, and many others, without the need for complex, slow, and error-prone processes for copying the data. Access data from multiple systems within a single query.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Impala provides low latency and high concurrency for BI/analytic queries on the Hadoop ecosystem, including Iceberg, open data formats, and most cloud storage options. Impala also scales linearly, even in multitenant environments. Impala is integrated with native Hadoop security and Kerberos for authentication, and via the Ranger module, you can ensure that the right users and applications are authorized for 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. For Apache Hive users, Impala utilizes the same metadata and ODBC driver. Like Hive, Impala supports SQL, so you don't have to worry about reinventing the implementation wheel. With Impala, more users, whether using SQL queries or BI applications, can interact with more data through a single repository and metadata stored from source through analysis.

Dremio delivers lightning-fast queries and a self-service semantic layer directly on your data lake storage. No moving data to proprietary data warehouses, no cubes, no aggregation tables or extracts. Just flexibility and control for data architects, and self-service for data consumers. Dremio technologies like Data Reflections, Columnar Cloud Cache (C3) and Predictive Pipelining work alongside Apache Arrow to make queries on your data lake storage very, very fast. An abstraction layer enables IT to apply security and business meaning, while enabling analysts and data scientists to explore data and derive new virtual datasets. Dremio’s semantic layer is an integrated, searchable catalog that indexes all of your metadata, so business users can easily make sense of your data. Virtual datasets and spaces make up the semantic layer, and are all indexed and searchable.

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Starburst helps you make better decisions with fast access to all your data; Without the complexity of data movement and copies. Your company has more data than ever before, but your data teams are stuck waiting to analyze it. Starburst unlocks access to data where it lives, no data movement required, giving your teams fast & accurate access to more data for analysis. Starburst Enterprise is a fully supported, production-tested and enterprise-grade distribution of open source Trino (formerly Presto® SQL). It improves performance and security while making it easy to deploy, connect, and manage your Trino environment. Through connecting to any source of data – whether it’s located on-premise, in the cloud, or across a hybrid cloud environment – Starburst lets your team use the analytics tools they already know & love while accessing data that lives anywhere.

Powered by Apache Doris, VeloDB is a modern data warehouse for lightning-fast analytics on real-time data at scale. Push-based micro-batch and pull-based streaming data ingestion within seconds. Storage engine with real-time upsert、append and pre-aggregation. Unparalleled performance in both real-time data serving and interactive ad-hoc queries. Not just structured but also semi-structured data. Not just real-time analytics but also batch processing. Not just run queries against internal data but also work as a federate query engine to access external data lakes and databases. Distributed design to support linear scalability. Whether on-premise deployment or cloud service, separation or integration of storage and compute, resource usage can be flexibly and efficiently adjusted according to workload requirements. Built on and fully compatible with open source Apache Doris. Support MySQL protocol, functions, and SQL for easy integration with other data tools.

Qubole is a simple, open, and secure Data Lake Platform for machine learning, streaming, and ad-hoc analytics. Our platform provides end-to-end services that reduce the time and effort required to run Data pipelines, Streaming Analytics, and Machine Learning workloads on any cloud. No other platform offers the openness and data workload flexibility of Qubole while lowering cloud data lake costs by over 50 percent. Qubole delivers faster access to petabytes of secure, reliable and trusted datasets of structured and unstructured data for Analytics and Machine Learning. Users conduct ETL, analytics, and AI/ML workloads efficiently in end-to-end fashion across best-of-breed open source engines, multiple formats, libraries, and languages adapted to data volume, variety, SLAs and organizational policies.

Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. For data engineers who struggle with managing multiple query languages and interfaces to siloed databases and storage, Presto is the fast and reliable engine that provides one simple ANSI SQL interface for all your data analytics and your open lakehouse. Different engines for different workloads means you will have to re-platform down the road. With Presto, you get 1 familar ANSI SQL language and 1 engine for your data analytics so you don't need to graduate to another lakehouse engine. Presto can be used for interactive and batch workloads, small and large amounts of data, and scales from a few to thousands of users. Presto gives you one simple ANSI SQL interface for all of your data in various siloed data systems, helping you join your data ecosystem together.

Tabular is an open table store from the creators of Apache Iceberg. Connect multiple computing engines and frameworks. Decrease query time and storage costs by up to 50%. Centralize enforcement of data access (RBAC) policies. Connect any query engine or framework, including Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python. Smart compaction, clustering, and other automated data services reduce storage costs and query times by up to 50%. Unify data access at the database or table. RBAC controls are simple to manage, consistently enforced, and easy to audit. Centralize your security down to the table. Tabular is easy to use plus it features high-powered ingestion, performance, and RBAC under the hood. Tabular gives you the flexibility to work with multiple “best of breed” compute engines based on their strengths. Assign privileges at the data warehouse database, table, or column level.

PuppyGraph empowers you to seamlessly query one or multiple data stores as a unified graph model. Graph databases are expensive, take months to set up, and need a dedicated team. Traditional graph databases can take hours to run multi-hop queries and struggle beyond 100GB of data. A separate graph database complicates your architecture with brittle ETLs and inflates your total cost of ownership (TCO). Connect to any data source anywhere. Cross-cloud and cross-region graph analytics. No complex ETLs or data replication is required. PuppyGraph enables you to query your data as a graph by directly connecting to your data warehouses and lakes. This eliminates the need to build and maintain time-consuming ETL pipelines needed with a traditional graph database setup. No more waiting for data and failed ETL processes. PuppyGraph eradicates graph scalability issues by separating computation and storage.

The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive. Apache Hive is an open source project run by volunteers at the Apache Software Foundation. Previously it was a subproject of Apache® Hadoop®, but has now graduated to become a top-level project of its own. We encourage you to learn about the project and contribute your expertise. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API.

Parallel query engine with time- and symbol-indexed data access. Extended SQL syntax with advanced filtering and aggregations. Consolidate quotes, trades, snapshots, and reference data in one place. Strategy backtesting on high-frequency data. Quantitative and market microstructure research. Granular transaction cost analysis and rollup reporting. Market surveillance and anomaly detection. Non-transparent ETF/ETN decomposition. FAST, SBE, and proprietary protocols. Plain text protocol. Consolidated and direct feeds. Built-in latency monitoring tools. End-of-day archives. ETL from institutional and retail financial data platforms. Parallel SQL engine with syntax extensions. Advanced filtering by trading session, auction stage, index composition. Optimized aggregates for OHLCV and VWAP calculations. Interactive SQL console with auto-completion. API endpoint for programmatic integration. Scheduled SQL reporting with email, file, and web delivery. JDBC and ODBC drivers.

Knowing of data wrangling habits, Polars exposes a complete Python API, including the full set of features to manipulate DataFrames using an expression language that will empower you to create readable and performant code. Polars is written in Rust, uncompromising in its choices to provide a feature-complete DataFrame API to the Rust ecosystem. Use it as a DataFrame library or as a query engine backend for your data models.

Palo helps enterprises to create the PB-level MPP architecture data warehouse service within several minutes and import the massive data from RDS, BOS, and BMR. Thus, Palo can perform the multi-dimensional analytics of big data. Palo is compatible with mainstream BI tools. Data analysts can analyze and display the data visually and gain insights quickly to assist decision-making. It has the industry-leading MPP query engine, with column storage, intelligent index,and vector execution functions. It can also provide in-library analytics, window functions, and other advanced analytics functions. You can create a materialized view and change the table structure without the suspension of service. It supports flexible and efficient data recovery.

Quasar's brain is QuasarDB, a high-performance, distributed, column-oriented timeseries database management system designed from the ground up to deliver real-time on petascale use cases. Up to 20X less disk usage. Quasardb ingestion and compression capabilities are unmatched. Up to 10,000X faster feature extraction. QuasarDB can extract features in real-time from the raw data, thanks to the combination of a built-in map/reduce query engine, an aggregation engine that leverages SIMD from modern CPUs, and stochastic indexes that use virtually no disk space. The most cost-effective timeseries solution, thanks to its ultra-efficient resource usage, the capability to leverage object storage (S3), unique compression technology, and fair pricing model. Quasar runs everywhere, from 32-bit ARM devices to high-end Intel servers, from Edge Computing to the cloud or on-premises.

ClickHouse is a fast open-source OLAP database management system. It is column-oriented and allows to generate analytical reports using SQL queries in real-time. ClickHouse's performance exceeds comparable column-oriented database management systems currently available on the market. It processes hundreds of millions to more than a billion rows and tens of gigabytes of data per single server per second. ClickHouse uses all available hardware to its full potential to process each query as fast as possible. Peak processing performance for a single query stands at more than 2 terabytes per second (after decompression, only used columns). In distributed setup reads are automatically balanced among healthy replicas to avoid increasing latency. ClickHouse supports multi-master asynchronous replication and can be deployed across multiple datacenters. All nodes are equal, which allows avoiding having single points of failure.

QuerySurge leverages AI to automate the data validation and ETL testing of Big Data, Data Warehouses, Business Intelligence Reports and Enterprise Apps/ERPs with full DevOps functionality for continuous testing. Use Cases - Data Warehouse & ETL Testing - Hadoop & NoSQL Testing - DevOps for Data / Continuous Testing - Data Migration Testing - BI Report Testing - Enterprise App/ERP Testing QuerySurge Features - Projects: Multi-project support - AI: automatically create datas validation tests based on data mappings - Smart Query Wizards: Create tests visually, without writing SQL - Data Quality at Speed: Automate the launch, execution, comparison & see results quickly - Test across 200+ platforms: Data Warehouses, Hadoop & NoSQL lakes, databases, flat files, XML, JSON, BI Reports - DevOps for Data & Continuous Testing: RESTful API with 60+ calls & integration with all mainstream solutions - Data Analytics & Data Intelligence:  Analytics dashboard & reports

EMR is an all-in-one enterprise-ready big data platform that provides cluster, job, and data management services based on open-source ecosystems, such as Hadoop, Spark, Kafka, Flink, and Storm. Alibaba Cloud Elastic MapReduce (EMR) is a big data processing solution that runs on the Alibaba Cloud platform. EMR is built on Alibaba Cloud ECS instances and is based on open-source Apache Hadoop and Apache Spark. EMR allows you to use the Hadoop and Spark ecosystem components, such as Apache Hive, Apache Kafka, Flink, Druid, and TensorFlow, to analyze and process data. You can use EMR to process data stored on different Alibaba Cloud data storage service, such as Object Storage Service (OSS), Log Service (SLS), and Relational Database Service (RDS). You can quickly create clusters without the need to configure hardware and software. All maintenance operations are completed on its Web interface.

Amazon Timestream is a fast, scalable, and serverless time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day up to 1,000 times faster and at as little as 1/10th the cost of relational databases. Amazon Timestream saves you time and cost in managing the lifecycle of time series data by keeping recent data in memory and moving historical data to a cost optimized storage tier based upon user defined policies. Amazon Timestream’s purpose-built query engine lets you access and analyze recent and historical data together, without needing to specify explicitly in the query whether the data resides in the in-memory or cost-optimized tier. Amazon Timestream has built-in time series analytics functions, helping you identify trends and patterns in your data in near real-time.

LlamaIndex is a “data framework” to help you build LLM apps. Connect semi-structured data from API's like Slack, Salesforce, Notion, etc. LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. LlamaIndex provides the key tools to augment your LLM applications with data. Connect your existing data sources and data formats (API's, PDF's, documents, SQL, etc.) to use with a large language model application. Store and index your data for different use cases. Integrate with downstream vector store and database providers. LlamaIndex provides a query interface that accepts any input prompt over your data and returns a knowledge-augmented response. Connect unstructured sources such as documents, raw text files, PDF's, videos, images, etc. Easily integrate structured data sources from Excel, SQL, etc. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.

doolytic is leading the way in big data discovery, the convergence of data discovery, advanced analytics, and big data. doolytic is rallying expert BI users to the revolution in self-service exploration of big data, revealing the data scientist in all of us. doolytic is an enterprise software solution for native discovery on big data. doolytic is based on best-of-breed, scalable, open-source technologies. Lightening performance on billions of records and petabytes of data. Structured, unstructured and real-time data from any source. Sophisticated advanced query capabilities for expert users, Integration with R for advanced and predictive applications. Search, analyze, and visualize data from any format, any source in real-time with the flexibility of Elastic. Leverage the power of Hadoop data lakes with no latency and concurrency issues. doolytic solves common BI problems and enables big data discovery without clumsy and inefficient workarounds.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.

Data-driven decision-making has unleashed a surge of business data and a rise in user demand to analyze it. This trend drives IT departments to migrate off expensive Enterprise Data Warehouses (EDW) toward cost-effective Big Data platforms like Hadoop or AWS. These new platforms come with a Total Cost of Ownership (TCO) that is about 10 times lower. They are not ideal for interactive BI applications, however, as they fail to match the high performance and user concurrency of legacy EDWs. For this exact reason, we developed Jethro. Customers use Jethro for interactive BI on Big Data. Jethro is a transparent middle tier that requires no changes to existing apps or data. It is self-driving with no maintenance required. Jethro is compatible with BI tools like Tableau, Qlik, and Microstrategy and is data source agnostic. Jethro delivers on the demands of business users allowing for thousands of concurrent users to run complicated queries over billions of records.

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL. Most results are delivered within seconds. With Athena, there’s no need for complex ETL jobs to prepare your data for analysis. This makes it easy for anyone with SQL skills to quickly analyze large-scale datasets. Athena is out-of-the-box integrated with AWS Glue Data Catalog, allowing you to create a unified metadata repository across various services, crawl data sources to discover schemas and populate your Catalog with new and modified table and partition definitions, and maintain schema versioning.

Perhaps you want to give your clients access to their LIMS data and reports via the web. AIS labPortal allows you to do just that. Paper copies of sample analyses needn’t be sent out in the post to customers. Using their unique login and security password, clients can access data from their computer, which is not only safer and less time-consuming but also more environmentally friendly. labPortal is a web-based portal that securely stores your clients’ sample information and data in the cloud, allowing them to easily access it instantly from their own desktop, tablet or phone. The labPortal interface is 'inbox' style which is simple and easy to use with an enhanced query engine, conditional highlighting and Microsoft Excel export. The software features a simple and easy-to-use sample registration form which allows users to pre-register samples online. Transcribing data is a time-consuming and tedious activity.

Processing and storing tabular datasets, e.g. from CSV or Parquet files. Large result set transfer to client. Large client/server installations for centralized enterprise data warehousing. Writing to a single database from multiple concurrent processes. DuckDB is a relational database management system (RDBMS). That means it is a system for managing data stored in relations. A relation is essentially a mathematical term for a table. Each table is a named collection of rows. Each row of a given table has the same set of named columns, and each column is of a specific data type. Tables themselves are stored inside schemas, and a collection of schemas constitutes the entire database that you can access.

With an in-memory, columnar database and MPP architecture, you can query billions of rows in seconds. Queries are distributed across all nodes in a cluster, providing linear scalability for more users and advanced analytics. MPP, in-memory, and columnar storage add up to the fastest database built for data analytics. With SaaS, cloud, on premises and hybrid deployment options you can analyze data wherever it lives. Automatic query tuning reduces maintenance and overhead. Seamless integrations and performance efficiency gets you more power at a fraction of normal infrastructure costs. Smart, in-memory query processing allowed this social networking company to boost performance, processing 10B data sets a year. A single data repository and speed engine to accelerate critical analytics, delivering improved patient outcome and bottom line.

EspressRepot ES (Enterprise Server) is a web and desktop-based software that allows users to develop stunning and interactive data visualization and reporting. The platform offers full Java EE integration, to draw data from data sources such as Bid Data (Hadoop, Spark, and MongoDB), ad-hoc queries and reports, online map support, mobile compatibility, alert monitor, and many other amazing features.

We make BI & Big Data analytics work easier and faster. Our goal is to empower business users and make always-busy business and heavily loaded BI specialists less dependent on each other when solving data-driven business problems. If you have ever experienced a lack of data you needed, time to consuming report generation or long queue to your BI expert, consider Querona. Querona uses a built-in Big Data engine to handle growing data volumes. Repeatable queries can be cached or calculated in advance. Optimization needs less effort as Querona automatically suggests query improvements. Querona empowers business analysts and data scientists by putting self-service in their hands. They can easily discover and prototype data models, add new data sources, experiment with query optimization and dig in raw data. Less IT is needed. Now users can get live data no matter where it is stored. If databases are too busy to be queried live, Querona will cache the data.

Whether you're working with a single table or multiple, you'll experience at least 300% better performance on StarRocks compared to other popular solutions. From streaming data to data capture, with a rich set of connectors, you can ingest data into StarRocks in real time for the freshest insights. A query engine that adapts to your use cases. Without moving your data or rewriting SQL, StarRocks provides the flexibility to scale your analytics on demand with ease. StarRocks enables a rapid journey from data to insight. StarRocks' performance is unmatched and provides a unified OLAP solution covering the most popular data analytics scenarios. Whether you're working with a single table or multiple, you'll experience at least 300% better performance on StarRocks compared to other popular solutions. StarRocks' built-in memory-and-disk-based caching framework is specifically designed to minimize the I/O overhead of fetching data from external storage to accelerate query performance.

ROBIN is the industry’s first hyper-converged Kubernetes platform for big data, databases, and AI/ML. The platform provides a self-service App-store experience for the deployment of any application, anywhere – runs on-premises in your private data center or in public-cloud (AWS, Azure, GCP) environments. Hyper-converged Kubernetes is a software-defined application orchestration framework that combines containerized storage, networking, compute (Kubernetes), and the application management layer into a single system. Our approach extends Kubernetes for data-intensive applications such as Hortonworks, Cloudera, Elastic stack, RDBMS, NoSQL databases, and AI/ML apps. Facilitates simpler and faster roll-out of critical Enterprise IT and LoB initiatives, such as containerization, cloud-migration, cost-consolidation, and productivity improvement. Solves the fundamental challenges of running big data and databases in Kubernetes.

Create relational or flat file databases with unlimited tables, fields, and rows. Includes a custom report builder. Interface with ODBC compatible databases and create custom reports for them. Create your own personal and custom databases. Some Highlights: - Filter tables instantly - Ultra simple graphical-user-interface - One click table and data form creation - Open up to 5 databases simultaneously - Export your data to comma separated files - Create custom reports for all your databases - Full helpfile to assist in creating database reports - Print tables and queries directly from the data grid - Supports any SQL standard that your ODBC compatible database requires Please install and run this database application with full administrator rights for best performance and user experience. Requires: . 1024x768 Display Size . Windows 98 / XP / 7 / 8 / 10 - 32bit and 64bit No Java or DotNet required. Green Energy Software. Saving the planet one bit at a time...

PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine. Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics.

The Unified Analytics Warehouse. Highest performing analytics and machine learning at extreme scale. As the criteria for data warehousing continues to evolve, tech research analysts are seeing new leaders in the drive for game-changing big data analytics. Vertica powers data-driven enterprises so they can get the most out of their analytics initiatives with advanced time-series and geospatial analytics, in-database machine learning, data lake integration, user-defined extensions, cloud-optimized architecture, and more. Our Under the Hood webcast series lets you to dive deep into Vertica features – delivered by Vertica engineers and technical experts – to find out what makes it the fastest and most scalable advanced analytical database on the market. From ride sharing apps and smart agriculture to predictive maintenance and customer analytics, Vertica supports the world’s leading data-driven disruptors in their pursuit of industry and business transformation.

Now that your data is in motion, it’s time to make sense of it. Stream processing enables you to derive instant insights from your data streams, but setting up the infrastructure to support it can be complex. That’s why Confluent developed ksqlDB, the database purpose-built for stream processing applications. Make your data immediately actionable by continuously processing streams of data generated throughout your business. ksqlDB’s intuitive syntax lets you quickly access and augment data in Kafka, enabling development teams to seamlessly create real-time innovative customer experiences and fulfill data-driven operational needs. ksqlDB offers a single solution for collecting streams of data, enriching them, and serving queries on new derived streams and tables. That means less infrastructure to deploy, maintain, scale, and secure. With less moving parts in your data architecture, you can focus on what really matters -- innovation.

Oracle Big Data Service makes it easy for customers to deploy Hadoop clusters of all sizes, with VM shapes ranging from 1 OCPU to a dedicated bare metal environment. Customers choose between high-performance NVmE storage or cost-effective block storage, and can grow or shrink their clusters. Quickly create Hadoop-based data lakes to extend or complement customer data warehouses, and ensure that all data is both accessible and managed cost-effectively. Query, visualize and transform data so data scientists can build machine learning models using the included notebook with its R, Python and SQL support. Move customer-managed Hadoop clusters to a fully-managed cloud-based service, reducing management costs and improving resource utilization.

Don’t let app, device, or game crashes get in the way of a great experience. Backtrace takes all the manual labor out of cross-platform crash and exception management so you can focus on shipping. Cross-platform callstack and event aggregation and monitoring. Process errors from panics, core dumps, minidumps, and during runtime across your stack with a single system. Backtrace generates structured, searchable error reports from your data. Automated analysis cuts down on time to resolution by surfacing important signals that lead engineers to crash root cause. Never worry about missing a clue with rich integrations into dashboards, notification, and workflow systems. Answer the questions that matter to you with Backtrace’s rich query engine. View a high-level overview of error frequency, prioritization, and trends across all your projects. Search through key data points and your own custom data across all your errors.

GeoSpock enables data fusion for the connected world with GeoSpock DB – the space-time analytics database. GeoSpock DB is a unique, cloud-native database optimised for querying for real-world use cases, able to fuse multiple sources of Internet of Things (IoT) data together to unlock its full value, whilst simultaneously reducing complexity and cost. GeoSpock DB enables efficient storage, data fusion, and rapid programmatic access to data, and allows you to run ANSI SQL queries and connect to analytics tools via JDBC/ODBC connectors. Users are able to perform analysis and share insights using familiar toolsets, with support for common BI tools (such as Tableau™, Amazon QuickSight™, and Microsoft Power BI™), and Data Science and Machine Learning environments (including Python Notebooks and Apache Spark). The database can also be integrated with internal applications and web services – with compatibility for open-source and visualisation libraries such as Kepler and Cesium.js.

Easily develop and run massively parallel data transformation and processing programs in U-SQL, R, Python, and .NET over petabytes of data. With no infrastructure to manage, you can process data on demand, scale instantly, and only pay per job. Process big data jobs in seconds with Azure Data Lake Analytics. There is no infrastructure to worry about because there are no servers, virtual machines, or clusters to wait for, manage, or tune. Instantly scale the processing power, measured in Azure Data Lake Analytics Units (AU), from one to thousands for each job. You only pay for the processing that you use per job. Act on all of your data with optimized data virtualization of your relational sources such as Azure SQL Database and Azure Synapse Analytics. Your queries are automatically optimized by moving processing close to the source data without data movement, which maximizes performance and minimizes latency.

Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture. Druid stores and compresses each column individually, and only needs to read the ones needed for a particular query, which supports fast scans, rankings, and groupBys. Druid creates inverted indexes for string values for fast search and filter. Out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures.

Panoply brings together a managed data warehouse with included, pre-built ELT data connectors, making it the easiest way to store, sync, and access all your business data. Our cloud data warehouse (built on Redshift or BigQuery), along with built-in data integrations to all major CRMs, databases, file systems, ad networks, web analytics tools, and more, will have you accessing usable data in less time, with a lower total cost of ownership. One platform with one easy price is all you need to get your business data up and running today. Panoply gives you unlimited access to data sources with prebuilt Snap Connectors and a Flex Connector that can bring in data from nearly any RestAPI. Panoply can be set up in minutes, requires zero ongoing maintenance, and provides online support including access to experienced data architects.

Hydrolix is a streaming data lake that combines decoupled storage, indexed search, and stream processing to deliver real-time query performance at terabyte-scale for a radically lower cost. CFOs love the 4x reduction in data retention costs. Product teams love 4x more data to work with. Spin up resources when you need them and scale to zero when you don’t. Fine-tune resource consumption and performance by workload to control costs. Imagine what you can build when you don’t have to sacrifice data because of budget. Ingest, enrich, and transform log data from multiple sources including Kafka, Kinesis, and HTTP. Return just the data you need, no matter how big your data is. Reduce latency and costs, eliminate timeouts, and brute force queries. Storage is decoupled from ingest and query, allowing each to independently scale to meet performance and budget targets. Hydrolix’s high-density compression (HDX) typically reduces 1TB of stored data to 55GB.

Accelerate AI innovation with cloud-native data integration on IBM Cloud Pak for data. AI-powered data integration, anywhere. Your AI and analytics are only as good as the data that fuels them. With a modern container-based architecture, IBM® DataStage® for IBM Cloud Pak® for Data delivers that high-quality data. It combines industry-leading data integration with DataOps, governance and analytics on a single data and AI platform. Automation accelerates administrative tasks to help reduce TCO. AI-based design accelerators and out-of-the-box integration with DataOps and data science services speed AI innovation. Parallelism and multicloud integration let you deliver trusted data at scale across hybrid or multicloud environments. Manage the data and analytics lifecycle on the IBM Cloud Pak for Data platform. Services include data science, event messaging, data virtualization and data warehousing. Parallel engine and automated load balancing.

We’re MotherDuck, a software company founded by a passionate flock of experienced data geeks. We’ve worked as leaders for some of the greatest companies in data. Scale-out is expensive and slow, let’s scale up. Big Data is dead, long live easy data. Your laptop is faster than your data warehouse. Why wait for the cloud? DuckDB slaps, so let’s supercharge it. When we founded MotherDuck we recognized that DuckDB might just be the next major game changer thanks to its ease of use, portability, lightning-fast performance, and rapid pace of community-driven innovation. At MotherDuck, we want to help the community, the DuckDB Foundation, and DuckDB Labs build greater awareness and adoption of DuckDB, whether users are working locally or want a serverless always-on way to execute their SQL. We are a world-class team of engineers and leaders with experience working on databases and cloud services at AWS, Databricks, Elastic, Facebook, Firebolt, Google BigQuery, Neo4j, SingleStore, and more.

AnswerDock is an AI-driven analytics platform for the enterprise. It provides answers to business users' questions and allows them to make better and faster decisions, without the need for data analysts. Get instant insights from your data warehouse via live query (available for Snowflake, Amazon Redshift, Microsoft Synapse, Google Bigquery). You can also upload excel files or connect to traditional relational databases (Mysql, SQL Server, etc.) and 3rd-party APIs such as Google Analytics. Try AnswerDock on a sample retail dataset. No need for registration or login. Try AnswerDock on your own data by signing up to the free version (all features). Using AnswerDock, business users create their own reports and dashboards by typing their questions, just like using a web search engine. Need a sales report? type Top 10 Sales People by growth in number of leads this quarter. AnswerDock runs the analysis and displays the optimal visualization instantly, its that simple.

Run popular open-source frameworks—including Apache Hadoop, Spark, Hive, Kafka, and more—using Azure HDInsight, a customizable, enterprise-grade service for open-source analytics. Effortlessly process massive amounts of data and get all the benefits of the broad open-source project ecosystem with the global scale of Azure. Easily migrate your big data workloads and processing to the cloud. Open-source projects and clusters are easy to spin up quickly without the need to install hardware or manage infrastructure. Big data clusters reduce costs through autoscaling and pricing tiers that allow you to pay for only what you use. Enterprise-grade security and industry-leading compliance with more than 30 certifications helps protect your data. Optimized components for open-source technologies such as Hadoop and Spark keep you up to date.