Alternatives to IBM Db2 Event Store

Amazon ElastiCache enables users to effortlessly establish, operate, and expand widely-used open-source compatible in-memory data stores in the cloud environment. It empowers the development of data-driven applications or enhances the efficiency of existing databases by allowing quick access to data through high throughput and minimal latency in-memory stores. This service is particularly favored for various real-time applications such as caching, session management, gaming, geospatial services, real-time analytics, and queuing. With fully managed options for Redis and Memcached, Amazon ElastiCache caters to demanding applications that necessitate response times in the sub-millisecond range. Functioning as both an in-memory data store and a cache, it is designed to meet the needs of applications that require rapid data retrieval. Furthermore, by utilizing a fully optimized architecture that operates on dedicated nodes for each customer, Amazon ElastiCache guarantees incredibly fast and secure performance for its users' critical workloads. This makes it an essential tool for businesses looking to enhance their application's responsiveness and scalability.

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, scalable upserts, 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. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

SingleStore, previously known as MemSQL, is a highly scalable and distributed SQL database that can operate in any environment. It is designed to provide exceptional performance for both transactional and analytical tasks while utilizing well-known relational models. This database supports continuous data ingestion, enabling operational analytics critical for frontline business activities. With the capacity to handle millions of events each second, SingleStore ensures ACID transactions and allows for the simultaneous analysis of vast amounts of data across various formats, including relational SQL, JSON, geospatial, and full-text search. It excels in data ingestion performance at scale and incorporates built-in batch loading alongside real-time data pipelines. Leveraging ANSI SQL, SingleStore offers rapid query responses for both current and historical data, facilitating ad hoc analysis through business intelligence tools. Additionally, it empowers users to execute machine learning algorithms for immediate scoring and conduct geoanalytic queries in real-time, thereby enhancing decision-making processes. Furthermore, its versatility makes it a strong choice for organizations looking to derive insights from diverse data types efficiently.

A streaming database is specifically designed to efficiently ingest, store, process, and analyze large volumes of data streams. This advanced data infrastructure integrates messaging, stream processing, and storage to enable real-time value extraction from your data. It continuously handles vast amounts of data generated by diverse sources, including sensors from IoT devices. Data streams are securely stored in a dedicated distributed streaming data storage cluster that can manage millions of streams. By subscribing to topics in HStreamDB, users can access and consume data streams in real-time at speeds comparable to Kafka. The system also allows for permanent storage of data streams, enabling users to replay and analyze them whenever needed. With a familiar SQL syntax, you can process these data streams based on event-time, similar to querying data in a traditional relational database. This functionality enables users to filter, transform, aggregate, and even join multiple streams seamlessly, enhancing the overall data analysis experience. Ultimately, the integration of these features ensures that organizations can leverage their data effectively and make timely decisions.

Riak®, TS is an enterprise-grade NoSQL Time Series Database that is specifically designed for IoT data and Time Series data. It can ingest, transform, store, and analyze massive amounts of time series information. Riak TS is designed to be faster than Cassandra. Riak TS masterless architecture can read and write data regardless of network partitions or hardware failures. Data is evenly distributed throughout the Riak ring. By default, there are three copies of your data. This ensures that at least one copy is available for reading operations. Riak TS is a distributed software system that does not have a central coordinator. It is simple to set up and use. It is easy to add or remove nodes from a cluster thanks to the masterless architecture. Riak TS's masterless architecture makes it easy for you to add or remove nodes from your cluster. Adding nodes made of commodity hardware to your cluster can help you achieve predictable and almost linear scale.

Achieve limitless data retention for Apache Kafka® with Confluent, empowering you to be infrastructure-enabled rather than constrained by outdated systems. Traditional technologies often force a choice between real-time processing and scalability, but event streaming allows you to harness both advantages simultaneously, paving the way for innovation and success. Have you ever considered how your rideshare application effortlessly analyzes vast datasets from various sources to provide real-time estimated arrival times? Or how your credit card provider monitors millions of transactions worldwide, promptly alerting users to potential fraud? The key to these capabilities lies in event streaming. Transition to microservices and facilitate your hybrid approach with a reliable connection to the cloud. Eliminate silos to ensure compliance and enjoy continuous, real-time event delivery. The possibilities truly are limitless, and the potential for growth is unprecedented.

Machbase is a leading time-series database designed for real-time storage and analysis of vast amounts of sensor data from various facilities. It stands out as the only database management system (DBMS) capable of processing and analyzing large datasets at remarkable speeds, showcasing its impressive capabilities. Experience the extraordinary processing speeds that Machbase offers! This innovative product allows for immediate handling, storage, and analysis of sensor information. It achieves rapid storage and querying of sensor data by integrating the DBMS directly into Edge devices. Additionally, it provides exceptional performance in data storage and extraction when operating on a single server. With the ability to configure multi-node clusters, Machbase offers enhanced availability and scalability. Furthermore, it serves as a comprehensive management solution for Edge computing, addressing device management, connectivity, and data handling needs effectively. In a fast-paced data-driven world, Machbase proves to be an essential tool for industries relying on real-time sensor data analysis.

Experience Apache Kafka offered as a fully managed service that avoids vendor lock-in while providing comprehensive features for constructing your streaming pipeline. You can establish a fully managed Kafka instance in under 10 minutes using our intuitive web console or programmatically through our API, CLI, Terraform provider, or Kubernetes operator. Seamlessly integrate it with your current technology infrastructure using more than 30 available connectors, and rest assured with comprehensive logs and metrics that come standard through our service integrations. This fully managed distributed data streaming platform can be deployed in any cloud environment of your choice. It’s perfectly suited for applications that rely on event-driven architectures, facilitating near-real-time data transfers and pipelines, stream analytics, and any situation where swift data movement between applications is essential. With Aiven’s hosted and expertly managed Apache Kafka, you can effortlessly set up clusters, add new nodes, transition between cloud environments, and update existing versions with just a single click, all while keeping an eye on performance through a user-friendly dashboard. Additionally, this service enables businesses to scale their data solutions efficiently as their needs evolve.

Apache DataFusion is a versatile and efficient query engine crafted in Rust, leveraging Apache Arrow for its in-memory data representation. It caters to developers engaged in creating data-focused systems, including databases, data frames, machine learning models, and real-time streaming applications. With its SQL and DataFrame APIs, DataFusion features a vectorized, multi-threaded execution engine that processes data streams efficiently and supports various partitioned data sources. It is compatible with several native formats such as CSV, Parquet, JSON, and Avro, and facilitates smooth integration with popular object storage solutions like AWS S3, Azure Blob Storage, and Google Cloud Storage. The architecture includes a robust query planner and an advanced optimizer that boasts capabilities such as expression coercion, simplification, and optimizations that consider distribution and sorting, along with automatic reordering of joins. Furthermore, DataFusion allows for extensive customization, enabling developers to incorporate user-defined scalar, aggregate, and window functions along with custom data sources and query languages, making it a powerful tool for diverse data processing needs. This adaptability ensures that developers can tailor the engine to fit their unique use cases effectively.

PolarDB-X has proven its reliability during the Tmall Double 11 shopping events and has assisted clients in various sectors, including finance, logistics, energy, e-commerce, and public services, in overcoming their business obstacles. It offers scalable storage solutions that can expand linearly to accommodate petabyte-scale demands, thereby eliminating the constraints associated with traditional standalone databases. Additionally, it features massively parallel processing (MPP) capabilities that greatly enhance the efficiency of performing complex analyses and executing queries on large datasets. Furthermore, it employs sophisticated algorithms to distribute data across multiple storage nodes, which effectively minimizes the amount of data held within individual tables. This advanced architecture not only optimizes performance but also ensures that businesses can handle their data needs flexibly and efficiently.

Handling and storing tabular data, such as that found in CSV or Parquet formats, is essential for data management. Transferring large result sets to clients is a common requirement, especially in extensive client/server frameworks designed for centralized enterprise data warehousing. Additionally, writing to a single database from various simultaneous processes poses its own set of challenges. DuckDB serves as a relational database management system (RDBMS), which is a specialized system for overseeing data organized into relations. In this context, a relation refers to a table, characterized by a named collection of rows. Each row within a table maintains a consistent structure of named columns, with each column designated to hold a specific data type. Furthermore, tables are organized within schemas, and a complete database comprises a collection of these schemas, providing structured access to the stored data. This organization not only enhances data integrity but also facilitates efficient querying and reporting across diverse datasets.

Efficient, large-scale log management and analysis in real time. Securely store, search, analyze, and receive alerts for all your log data and events effortlessly. Ingest custom logs from any origin. This is a fully managed service capable of handling exabyte-scale application and infrastructure logs. Experience real-time analysis of your log data. It is compatible with Google Cloud services and seamlessly integrates with Cloud Monitoring, Error Reporting, and Cloud Trace, enabling you to swiftly diagnose issues throughout your applications and infrastructure. With ingestion latency measured in sub-seconds and an impressive ingestion rate of terabytes per second, you can safely accumulate all logs from various sources without any management burden. Enhance your capabilities by merging Cloud Logging with BigQuery for in-depth analysis, and utilize log-based metrics to create real-time dashboards in Cloud Monitoring. Additionally, this comprehensive management solution simplifies the process of maintaining data integrity while optimizing system performance.

Oracle Big Data SQL Cloud Service empowers companies to swiftly analyze information across various platforms such as Apache Hadoop, NoSQL, and Oracle Database, all while utilizing their existing SQL expertise, security frameworks, and applications, achieving remarkable performance levels. This solution streamlines data science initiatives and facilitates the unlocking of data lakes, making the advantages of Big Data accessible to a wider audience of end users. It provides a centralized platform for users to catalog and secure data across Hadoop, NoSQL systems, and Oracle Database. With seamless integration of metadata, users can execute queries that combine data from Oracle Database with that from Hadoop and NoSQL databases. Additionally, the service includes utilities and conversion routines that automate the mapping of metadata stored in HCatalog or the Hive Metastore to Oracle Tables. Enhanced access parameters offer administrators the ability to customize column mapping and govern data access behaviors effectively. Furthermore, the capability to support multiple clusters allows a single Oracle Database to query various Hadoop clusters and NoSQL systems simultaneously, thereby enhancing data accessibility and analytics efficiency. This comprehensive approach ensures that organizations can maximize their data insights without compromising on performance or security.

Apache Accumulo enables users to efficiently store and manage extensive data sets across a distributed cluster. It relies on Apache Hadoop's HDFS for data storage and utilizes Apache ZooKeeper to achieve consensus among nodes. While many users engage with Accumulo directly, it also serves as a foundational data store for various open-source projects. To gain deeper insights into Accumulo, you can explore the Accumulo tour, consult the user manual, and experiment with the provided example code. Should you have any inquiries, please do not hesitate to reach out to us. Accumulo features a programming mechanism known as Iterators, which allows for the modification of key/value pairs at different stages of the data management workflow. Each key/value pair within Accumulo is assigned a unique security label that restricts query outcomes based on user permissions. The system operates on a cluster configuration that can incorporate one or more HDFS instances, providing flexibility as data storage needs evolve. Additionally, nodes within the cluster can be dynamically added or removed in response to changes in the volume of data stored, enhancing scalability and resource management.

Keen is a fully managed event streaming platform. Our real-time data pipeline, built on Apache Kafka, makes it easy to collect large amounts of event data. Keen's powerful REST APIs and SDKs allow you to collect event data from any device connected to the internet. Our platform makes it possible to securely store your data, reducing operational and delivery risks with Keen. Apache Cassandra's storage infrastructure ensures data is completely secure by transferring it via HTTPS and TLS. The data is then stored with multilayer AES encryption. Access Keys allow you to present data in an arbitrary way without having to re-architect or re-architect the data model. Role-based Access Control allows for completely customizable permission levels, down to specific queries or data points.

Develop high-speed, data-centric applications that can dynamically adapt to performance needs regardless of scale. Leverage the distinctive technology of Apache Geode, which integrates sophisticated methods for data replication, partitioning, and distributed processing. With a database-like consistency model, Apache Geode guarantees dependable transaction handling and employs a shared-nothing architecture that supports remarkably low latency, even under high concurrency. The platform allows for seamless data partitioning (sharding) and replication across nodes, enabling performance to grow in accordance with demand. Reliability is bolstered by maintaining redundant in-memory copies along with disk-based persistence. Additionally, it features rapid write-ahead logging (WAL) persistence, optimized for quick parallel recovery of individual nodes or the entire cluster, ensuring robust performance even during failures. This combination of features not only enhances efficiency but also significantly improves overall system resilience.

OpenText Analytics Database is a cutting-edge analytics platform designed to accelerate decision-making and operational efficiency through fast, real-time data processing and advanced machine learning. Organizations benefit from its flexible deployment options, including on-premises, hybrid, and multi-cloud environments, enabling them to tailor analytics infrastructure to their specific needs and lower overall costs. The platform’s massively parallel processing (MPP) architecture delivers lightning-fast query performance across large, complex datasets. It supports columnar storage and data lakehouse compatibility, allowing seamless analysis of data stored in various formats such as Parquet, ORC, and AVRO. Users can interact with data using familiar languages like SQL, R, Python, Java, and C/C++, making it accessible for both technical and business users. In-database machine learning capabilities allow for building and deploying predictive models without moving data, providing real-time insights. Additional analytics functions include time series, geospatial, and event-pattern matching, enabling deep and diverse data exploration. OpenText Analytics Database is ideal for organizations looking to harness AI and analytics to drive smarter business decisions.

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.

You can set up a fully functional cluster in just a few minutes. The database configurations are pre-optimized based on the selected cluster size. Should the demand for your cluster rise, it’s easy to either add new servers or boost their existing capacity within minutes. Redis utilizes a key-value data storage format, accommodating various types such as strings, arrays, dictionaries, sets, and bitmasks, among others. Operating primarily in RAM, Redis is ideal for scenarios that demand rapid responses or involve executing numerous operations on a relatively small dataset. The contents of the database are secured with GPG encryption for backups. Additionally, data protection adheres to local regulations, GDPR, and ISO standards. You can also set a time limit for the Yandex Managed Service for Redis to automatically purge data, which helps in optimizing storage expenses. This feature allows for better management of resources while ensuring compliance and security.

Introducing a premier high-performance distributed SQL database that is open source and designed specifically for cloud-native environments, ideal for powering applications on a global internet scale. Experience minimal latency, often in the single-digit milliseconds, allowing you to create incredibly fast cloud applications by executing queries directly from the database itself. Handle immense workloads effortlessly, achieving millions of transactions per second and accommodating several terabytes of data on each node. With geo-distribution capabilities, you can deploy your database across various regions and cloud platforms, utilizing synchronous or multi-master replication for optimal performance. Tailored for modern cloud-native architectures, YugabyteDB accelerates the development, deployment, and management of applications like never before. Enjoy enhanced developer agility by tapping into the full capabilities of PostgreSQL-compatible SQL alongside distributed ACID transactions. Maintain resilient services with assured continuous availability, even amidst failures in compute, storage, or network infrastructure. Scale your resources on demand, easily adding or removing nodes as needed, and eliminate the necessity for over-provisioned clusters. Additionally, benefit from significantly reduced user latency, ensuring a seamless experience for your app users.

CODA (Consumer Data Analysis) is an analytics solution specifically designed for the Fast-Moving Consumer Goods (FMCG) and Retail industries, focusing on the enhancement, clarification, and examination of vast datasets. With CODA, users can gain insights into various aspects such as: Market share of brands and competitors, Distribution metrics, Comprehensive analysis of categories and sub-categories, Performance metrics for leading stores and SKUs, Analysis of market trends, Evaluation of New Product Development (NPD), Assessment of pricing strategies, Impact of promotions on sales growth, Geographical performance trends, Analysis of different store formats, Tracking outlet shares alongside shelf share metrics. This tool empowers businesses to make informed decisions by leveraging extensive data analysis.

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

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.

The IBM StoredIQ® Suite addresses various challenges related to data discovery, records management, compliance, storage optimization, and data migration. It offers a thorough assessment of unstructured data right where it resides, granting organizations the insight necessary to make better business decisions. For legal professionals, IBM StoredIQ for Legal streamlines the electronic discovery (eDiscovery) process through a systematic approach. Additionally, organizations can automate their policies using IBM StoredIQ Policy, which is included in the StoredIQ Suite. This in-place data management allows businesses to identify and manage unstructured data without relocating it to separate repositories or applications. The suite features a robust search capability that enhances the analysis of vast amounts of unstructured content. Ultimately, businesses can achieve a clearer and more comprehensive understanding of their extensive corporate data. With these tools, organizations are better equipped to navigate the complexities of their data landscape effectively.

PartiQL extends SQL in a manner that is straightforward, allowing nested data to be treated as integral components and enabling a smooth integration with SQL itself. This capability facilitates intuitive operations such as filtering, joining, and aggregating various types of data, including structured, semistructured, and nested datasets. By decoupling the syntax and semantics of queries from the actual data format or storage system, PartiQL provides a cohesive querying experience across diverse data stores and formats. It empowers users to engage with data irrespective of the presence of a standard schema. Additionally, the components of PartiQL—including its syntax, semantics, embedded reference interpreter, command-line interface, testing framework, and associated tests—are distributed under the Apache License, version 2.0. This licensing grants users the freedom to use, modify, and share their contributions while adhering to their preferred terms. Thus, the overall design of PartiQL enhances accessibility and flexibility in data management across various platforms.

AnalyticDB for MySQL is an efficient data warehousing solution that boasts security, stability, and user-friendliness. This platform facilitates the creation of online statistical reports and multidimensional analysis applications while supporting real-time data warehousing. Utilizing a distributed computing framework, AnalyticDB for MySQL leverages the cloud’s elastic scaling to process vast amounts of data, handling tens of billions of records instantaneously. It organizes data according to relational models and employs SQL for flexible computation and analysis. Additionally, the service simplifies database management, allowing users to scale nodes and adjust instance sizes with ease. With its suite of visualization and ETL tools, it enhances enterprise data processing significantly. Moreover, this system enables rapid multidimensional analysis, offering the capability to sift through extensive datasets in mere milliseconds. It is a powerful resource for organizations looking to optimize their data strategies and gain insights quickly.

Concentrate on creating applications for processing data streams instead of spending time on infrastructure upkeep. The Managed Service for Apache Kafka takes care of Zookeeper brokers and clusters, handling tasks such as configuring the clusters and performing version updates. To achieve the desired level of fault tolerance, distribute your cluster brokers across multiple availability zones and set an appropriate replication factor. This service continuously monitors the metrics and health of the cluster, automatically replacing any node that fails to ensure uninterrupted service. You can customize various settings for each topic, including the replication factor, log cleanup policy, compression type, and maximum message count, optimizing the use of computing, network, and disk resources. Additionally, enhancing your cluster's performance is as simple as clicking a button to add more brokers, and you can adjust the high-availability hosts without downtime or data loss, allowing for seamless scalability. By utilizing this service, you can ensure that your applications remain efficient and resilient amidst any unforeseen challenges.

Enhance your metrics and alerting capabilities using a top-tier open-source monitoring tool. Prometheus inherently organizes all data as time series, which consist of sequences of timestamped values associated with the same metric and a specific set of labeled dimensions. In addition to the stored time series, Prometheus has the capability to create temporary derived time series based on query outcomes. The tool features a powerful query language known as PromQL (Prometheus Query Language), allowing users to select and aggregate time series data in real time. The output from an expression can be displayed as a graph, viewed in tabular format through Prometheus’s expression browser, or accessed by external systems through the HTTP API. Configuration of Prometheus is achieved through a combination of command-line flags and a configuration file, where the flags are used to set immutable system parameters like storage locations and retention limits for both disk and memory. This dual method of configuration ensures a flexible and tailored monitoring setup that can adapt to various user needs. For those interested in exploring this robust tool, further details can be found at: https://sourceforge.net/projects/prometheus.mirror/

Focus your efforts on your project, and we will manage the upkeep of the database, which includes software backups, continuous monitoring, ensuring fault tolerance, and applying updates. ClickHouse excels at processing large datasets in real-time, and its columnar storage approach significantly reduces storage requirements through efficient data compression. All database connections are secured with TLS encryption to maintain confidentiality. Additionally, we adhere to local regulations, GDPR, and ISO industry standards to protect your data. You can visualize the data architecture within your ClickHouse cluster and execute SQL queries directly from the management console. Furthermore, the service offers data replication across database hosts, both within and across availability zones, and seamlessly transitions the load to a backup replica in case of any failures, thus enhancing reliability. This comprehensive approach ensures that your data remains both accessible and secure during any unexpected incidents.

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.

In-Memory Computing Platform. Digital world is different. Microseconds are important. The world's most important organizations rely on us for powering their most sensitive applications at scale. If they meet the current requirement for immediate access, new data-enabled apps can transform your business. Hazelcast solutions can be used to complement any database and deliver results that are much faster than traditional systems of record. Hazelcast's distributed architecture ensures redundancy and continuous cluster up-time, as well as always available data to support the most demanding applications. The capacity grows with demand without compromising performance and availability. The cloud delivers the fastest in-memory data grid and third-generation high speed event processing.

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.

KX Insights serves as a cloud-native platform that provides essential real-time performance analytics and actionable intelligence continuously. By utilizing advanced techniques such as complex event processing, rapid analytics, and machine learning interfaces, it facilitates swift decision-making and automates responses to events in mere fractions of a second. The migration to the cloud encompasses not only storage and computational flexibility but also includes a comprehensive array of elements: data, tools, development, security, connectivity, operations, and maintenance. KX empowers organizations to harness this cloud capability, enabling them to make more informed and insightful decisions by seamlessly integrating real-time analytics into their operational frameworks. Additionally, KX Insights adheres to industry standards, promoting openness and interoperability with diverse technologies, which accelerates the delivery of insights in a cost-effective manner. Its architecture is based on microservices, designed for efficiently capturing, storing, and processing high-volume and high-velocity data utilizing established cloud standards, services, and protocols, ensuring optimal performance and scalability. This innovative approach not only enhances operational efficiency but also positions businesses to adapt swiftly to changing market dynamics.

Kwil represents a groundbreaking approach as the first decentralized SQL database owned by the community, specifically designed to aid in the creation of sophisticated decentralized applications (dApps) and protocols. By offering a highly scalable SQL database that is familiar to Web2 developers, Kwil accelerates the dApp development process significantly. Traditionally, developers have had to navigate intricate and inefficient blockchain-based data management systems to operate within a decentralized framework. However, KwilDB streamlines the development experience, providing a cost-effective and scalable solution for managing extensive data storage and querying needs. This capability not only simplifies decentralized data management but also allows dApps to effortlessly integrate with data from various other applications. The advantages of building on KwilDB include dramatically reduced development times, the capacity to perform complex queries across vast datasets swiftly, and improved access to data from diverse sources, fostering a high level of composability. Ultimately, KwilDB transforms the landscape of decentralized application development by making it more efficient and accessible for developers.

IBM Event Streams is a comprehensive event streaming service based on Apache Kafka, aimed at assisting businesses in managing and reacting to real-time data flows. It offers features such as machine learning integration, high availability, and secure deployment in the cloud, empowering organizations to develop smart applications that respond to events in real time. The platform is designed to accommodate multi-cloud infrastructures, disaster recovery options, and geo-replication, making it particularly suitable for critical operational tasks. By facilitating the construction and scaling of real-time, event-driven solutions, IBM Event Streams ensures that data is processed with speed and efficiency, ultimately enhancing business agility and responsiveness. As a result, organizations can harness the power of real-time data to drive innovation and improve decision-making processes.

This robust enterprise platform, built on Apache Ignite, delivers lightning-fast in-memory performance and extensive scalability for data-heavy applications, ensuring real-time access across various datastores and applications. Transitioning from Ignite to GridGain requires no code modifications, allowing for secure deployment of clusters on a global scale without experiencing any downtime. You can conduct rolling upgrades on your production clusters without affecting application availability, and replicate data across geographically dispersed data centers to balance workloads and mitigate the risk of outages in specific regions. Your data remains secure both at rest and in transit, while compliance with security and privacy regulations is guaranteed. Seamless integration with your organization’s existing authentication and authorization frameworks is straightforward, and comprehensive auditing of data and user activities can be enabled. Additionally, you can establish automated schedules for both full and incremental backups, ensuring that restoring your cluster to its most stable state is achievable through snapshots and point-in-time recovery. This platform not only promotes efficiency but also enhances resilience and security for all data operations.

TimescaleDB brings the power of PostgreSQL to time-series and event data at any scale. It extends standard Postgres with features like automatic time-based partitioning (hypertables), incremental materialized views, and native time-series functions, making it the most efficient way to handle analytical workloads. Designed for use cases like IoT, DevOps monitoring, crypto markets, and real-time analytics, it ingests millions of rows per second while maintaining sub-second query speeds. Developers can run complex time-based queries, joins, and aggregations using familiar SQL syntax — no new language or database model required. Built-in compression ensures long-term data retention without high storage costs, and automated data management handles rollups and retention policies effortlessly. Its hybrid storage architecture merges row-based performance for live data with columnar efficiency for historical queries. Open-source and 100% PostgreSQL compatible, TimescaleDB integrates with Kafka, S3, and the entire Postgres ecosystem. Trusted by global enterprises, it delivers the performance of a purpose-built time-series system without sacrificing Postgres reliability or flexibility.

Evaluate the effectiveness of the Greenplum Database Management System (DBMS) by utilizing the monitoring and query management features available in the command center, which also allows for the viewing and downloading of query and session histories. The Hybrid Storage feature within Yandex Managed Service for Greenplum® seamlessly integrates with Object Storage, enabling users to efficiently manage hybrid storage with automatic transfers of data to cold storage. You can set up a fully functional cluster in a matter of minutes, with database settings tailored to the chosen cluster size, although adjustments can be made if needed. The integration with the DataLens Business Intelligence system facilitates the creation of reports, charts, and dashboards directly from the service using data stored in Greenplum. Furthermore, all connections to the DBMS are secured with encryption through the TLS protocol, ensuring data protection. Our infrastructure complies with local regulations, GDPR, industry-specific ISO standards, and PCI DSS security requirements, providing peace of mind regarding data safety and compliance. Overall, this setup not only enhances operational efficiency but also prioritizes security and regulatory adherence.

A cloud-based data science platform provides meticulously curated and refined data, primed for immediate use. Companies encounter considerable hurdles, limited resources, and elevated expenses when handling their data before they can extract any meaningful insights. The data undergoes a process of ingestion, cleansing, storage, and access, culminating in analysis, which is where true value is derived. Our solution empowers clients to concentrate on their primary business functions instead of the costly, resource-intensive data lifecycle, as we manage those complexities for them. Additionally, our cloud-native platform supports real-time streaming analytics, capitalizing on the advantages of cloud architecture, allowing INQDATA to deliver swift and scalable access to both historical and real-time data while eliminating infrastructure complexities. This approach not only enhances efficiency but also ensures that businesses can adapt quickly to their evolving data needs.

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.

Introducing an innovative approach to handling enterprise file data at an extensive scale from any location. Our cloud-native file data solution offers unparalleled scale and efficiency, effortlessly accommodating your most demanding workloads while maintaining remarkable simplicity. Qumulo Core serves as a robust file data platform that empowers you to store, manage, and develop workflows and applications using data in its native file format, all while operating seamlessly across both on-premises and cloud infrastructures. You can securely manage petabytes of active file data within a single namespace, benefiting from intelligent scaling capabilities. Additionally, you can easily oversee operations with real-time analytics on every file and user, which enhances your IT management. With a versatile API and support for multiple protocols, constructing automated workflows and applications is straightforward. Now, managing the entire data lifecycle—from ingestion to transformation, publishing, and archiving—has never been easier, allowing for greater efficiency and productivity in your organization.

FlashGrid offers innovative software solutions aimed at boosting both the reliability and efficiency of critical Oracle databases across a range of cloud environments, such as AWS, Azure, and Google Cloud. By implementing active-active clustering through Oracle Real Application Clusters (RAC), FlashGrid guarantees an impressive 99.999% uptime Service Level Agreement (SLA), significantly reducing the risk of business interruptions that could arise from database outages. Their sophisticated architecture is designed to support multi-availability zone deployments, providing robust protection against potential data center failures and regional disasters. Additionally, FlashGrid's Cloud Area Network software enables the creation of high-speed overlay networks, complete with advanced features for high availability and performance management. Their Storage Fabric software plays a crucial role by converting cloud storage into shared disks that can be accessed by all nodes within a cluster. Furthermore, the FlashGrid Read-Local technology efficiently decreases storage network overhead by allowing read operations to be served directly from locally attached disks, ultimately leading to improved overall system performance. This comprehensive approach positions FlashGrid as a vital player in ensuring seamless database operations in the cloud.

Red Hat® OpenShift® Streams for Apache Kafka is a cloud-managed service designed to enhance the developer experience for creating, deploying, and scaling cloud-native applications, as well as for modernizing legacy systems. This service simplifies the processes of creating, discovering, and connecting to real-time data streams, regardless of their deployment location. Streams play a crucial role in the development of event-driven applications and data analytics solutions. By enabling seamless operations across distributed microservices and handling large data transfer volumes with ease, it allows teams to leverage their strengths, accelerate their time to value, and reduce operational expenses. Additionally, OpenShift Streams for Apache Kafka features a robust Kafka ecosystem and is part of a broader suite of cloud services within the Red Hat OpenShift product family, empowering users to develop a diverse array of data-driven applications. With its powerful capabilities, this service ultimately supports organizations in navigating the complexities of modern software development.

Introducing an advanced GPU-accelerated database designed to enhance data performance at scale, Brytlyt integrates AI capabilities with an analytics visualization workbench, enabling the generation of smarter intelligence in a fraction of the time. As leaders in the market with a forward-thinking global vision, we recognize the significance of data-driven decision-making and provide rapidly accelerated analytics that deliver millisecond performance. With Brytlyt, you can uncover insights from vast datasets at the speed of thought. Our comprehensive platform ensures that you receive critical business insights and support for your decisions. Built on PostgreSQL, it allows for seamless analysis of larger datasets, facilitating fast and effortless data processing. Extract meaningful intelligence in mere milliseconds while managing extensive data without losing any details. Our system is designed to adapt and customize as your data needs evolve, ensuring smooth integration into your existing infrastructures. Brytlyt not only empowers you to discover value when you need it but also positions you ahead of the competition, making data a true asset for your business. The transformative potential of our platform paves the way for innovative strategies and smarter business operations.

Invantive's complimentary Query Tool offers immediate Operational Intelligence (OI) for your entire organization. This tool grants you access to your live data warehouse and databases hosted on platforms like MySQL, Oracle, SQL Server, Teradata, and IBM DB2/UDB, among others. It facilitates the quick storage, organization, and retrieval of your operational data. With the optional repository provided by Invantive Producer, users can effortlessly transfer, assemble, integrate, and access data from a variety of sources. This functionality allows for the extraction and analysis of operational data related to various aspects, including project execution, manufacturing processes, software development, and service operations. The Invantive Query Tool enables you to execute SQL and Oracle PL/SQL query programs, providing crucial real-time insights into your business activities. Additionally, you'll be equipped to run intricate queries that help monitor operational tasks, ensure compliance with business regulations, identify potential risks, and make informed decisions. This tool ultimately enhances your ability to leverage data effectively for strategic planning and operational excellence.