Apache Superset Integrations

BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises. Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.

Snowflake offers a unified AI Data Cloud platform that transforms how businesses store, analyze, and leverage data by eliminating silos and simplifying architectures. It features interoperable storage that enables seamless access to diverse datasets at massive scale, along with an elastic compute engine that delivers leading performance for a wide range of workloads. Snowflake Cortex AI integrates secure access to cutting-edge large language models and AI services, empowering enterprises to accelerate AI-driven insights. The platform’s cloud services automate and streamline resource management, reducing complexity and cost. Snowflake also offers Snowgrid, which securely connects data and applications across multiple regions and cloud providers for a consistent experience. Their Horizon Catalog provides built-in governance to manage security, privacy, compliance, and access control. Snowflake Marketplace connects users to critical business data and apps to foster collaboration within the AI Data Cloud network. Serving over 11,000 customers worldwide, Snowflake supports industries from healthcare and finance to retail and telecom.

Hevo Data is a no-code, bi-directional data pipeline platform specially built for modern ETL, ELT, and Reverse ETL Needs. It helps data teams streamline and automate org-wide data flows that result in a saving of ~10 hours of engineering time/week and 10x faster reporting, analytics, and decision making. The platform supports 100+ ready-to-use integrations across Databases, SaaS Applications, Cloud Storage, SDKs, and Streaming Services. Over 500 data-driven companies spread across 35+ countries trust Hevo for their data integration needs.

SQLite is a C-language library that offers a compact, efficient, and reliable SQL database engine that is fully featured. Recognized as the most popular database engine globally, SQLite is embedded in every mobile device and the majority of computers, while also being included in a myriad of applications that are used daily by individuals. Operating as an in-process library, SQLite provides a self-sufficient, serverless, and zero-configuration transactional SQL database engine. The source code of SQLite resides in the public domain, making it available for anyone to use freely, whether for commercial or personal purposes. With its extensive deployment and integration into numerous applications, SQLite stands out as an invaluable tool for developers in various high-profile projects. Its versatility and ease of use contribute to its unmatched popularity in the database landscape.

MySQL stands out as the most widely used open source database globally. Thanks to its established track record in performance, dependability, and user-friendliness, it has emerged as the preferred database for web applications, powering notable platforms such as Facebook, Twitter, and YouTube, alongside the top five websites. Furthermore, MySQL is also highly favored as an embedded database solution, being distributed by numerous independent software vendors and original equipment manufacturers. Its versatility and robust features contribute to its widespread adoption across various industries.

Microsoft SQL Server 2019 incorporates both intelligence and security, providing users with added features at no additional cost while ensuring top-tier performance and adaptability for on-premises requirements. You can seamlessly transition to the cloud, taking full advantage of its efficiency and agility without the need to alter your existing code. By leveraging Azure, you can accelerate insight generation and predictive analytics. Development is flexible, allowing you to utilize your preferred technologies, including open-source options, supported by Microsoft's advancements. The platform enables easy data integration into your applications and offers a comprehensive suite of cognitive services that facilitate the creation of human-like intelligence, regardless of data volume. The integration of AI is intrinsic to the data platform, allowing for quicker insight extraction from both on-premises and cloud-stored data. By combining your unique enterprise data with global data, you can foster an organization that is driven by intelligence. The dynamic data platform provides a consistent user experience across various environments, expediting the time it takes to bring innovations to market; this allows you to develop your applications and deploy them in any environment you choose, enhancing overall operational efficiency.

ClickHouse is an efficient, open-source OLAP database management system designed for high-speed data processing. Its column-oriented architecture facilitates the creation of analytical reports through real-time SQL queries. In terms of performance, ClickHouse outshines similar column-oriented database systems currently on the market. It has the capability to handle hundreds of millions to over a billion rows, as well as tens of gigabytes of data, on a single server per second. By maximizing the use of available hardware, ClickHouse ensures rapid query execution. The peak processing capacity for individual queries can exceed 2 terabytes per second, considering only the utilized columns after decompression. In a distributed environment, read operations are automatically optimized across available replicas to minimize latency. Additionally, ClickHouse features multi-master asynchronous replication, enabling deployment across various data centers. Each node operates equally, effectively eliminating potential single points of failure and enhancing overall reliability. This robust architecture allows organizations to maintain high availability and performance even under heavy workloads.

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

Real-time analytics on raw data. Live ingest from S3, DynamoDB, DynamoDB and more. Raw data can be accessed as SQL tables. In minutes, you can create amazing data-driven apps and live dashboards. Rockset is a serverless analytics and search engine that powers real-time applications and live dashboards. You can directly work with raw data such as JSON, XML and CSV. Rockset can import data from real-time streams and data lakes, data warehouses, and databases. You can import real-time data without the need to build pipelines. Rockset syncs all new data as it arrives in your data sources, without the need to create a fixed schema. You can use familiar SQL, including filters, joins, and aggregations. Rockset automatically indexes every field in your data, making it lightning fast. Fast queries are used to power your apps, microservices and live dashboards. Scale without worrying too much about servers, shards or pagers.

PostgresML serves as a comprehensive platform integrated within a PostgreSQL extension, allowing users to construct models that are not only simpler and faster but also more scalable directly within their database environment. Users can delve into the SDK and utilize open-source models available in our hosted database for experimentation. The platform enables a seamless automation of the entire process, from generating embeddings to indexing and querying, which facilitates the creation of efficient knowledge-based chatbots. By utilizing various natural language processing and machine learning techniques, including vector search and personalized embeddings, users can enhance their search capabilities significantly. Additionally, it empowers businesses to analyze historical data through time series forecasting, thereby unearthing vital insights. With the capability to develop both statistical and predictive models, users can harness the full potential of SQL alongside numerous regression algorithms. The integration of machine learning at the database level allows for quicker result retrieval and more effective fraud detection. By abstracting the complexities of data management throughout the machine learning and AI lifecycle, PostgresML permits users to execute machine learning and large language models directly on a PostgreSQL database, making it a robust tool for data-driven decision-making. Ultimately, this innovative approach streamlines processes and fosters a more efficient use of data resources.

DataHub is a versatile open-source metadata platform crafted to enhance data discovery, observability, and governance within various data environments. It empowers organizations to easily find reliable data, providing customized experiences for users while avoiding disruptions through precise lineage tracking at both the cross-platform and column levels. By offering a holistic view of business, operational, and technical contexts, DataHub instills trust in your data repository. The platform features automated data quality assessments along with AI-driven anomaly detection, alerting teams to emerging issues and consolidating incident management. With comprehensive lineage information, documentation, and ownership details, DataHub streamlines the resolution of problems. Furthermore, it automates governance processes by classifying evolving assets, significantly reducing manual effort with GenAI documentation, AI-based classification, and intelligent propagation mechanisms. Additionally, DataHub's flexible architecture accommodates more than 70 native integrations, making it a robust choice for organizations seeking to optimize their data ecosystems. This makes it an invaluable tool for any organization looking to enhance their data management capabilities.

Firebird is a relational database that incorporates numerous features compliant with ANSI SQL standards and is compatible with Linux, Windows, and multiple Unix systems. It boasts remarkable concurrency, impressive performance, and robust support for stored procedures and triggers, making it a popular choice for various applications. Since its inception in 1981, Firebird has been utilized in production environments under different names. The Firebird Project operates independently, consisting of C and C++ developers, technical advisors, and supporters who contribute to the ongoing development of this multi-platform relational database management system, which is based on the source code released by Inprise Corp (now Borland Software Corp) on July 25, 2000. Moreover, the Firebird Project provides a wealth of documentation for users, developers, and administrators, ranging from beginner Quick Start guides to in-depth expert articles that explore numerous aspects of the Firebird system. This extensive resource ensures that anyone working with Firebird can find the information they need to optimize their experience.

IBM Db2 encompasses a suite of data management solutions, prominently featuring the Db2 relational database. These offerings incorporate AI-driven functionalities designed to streamline the management of both structured and unstructured data across various on-premises and multicloud settings. By simplifying data accessibility, the Db2 suite empowers businesses to leverage the advantages of AI effectively. Most components of the Db2 family are integrated within the IBM Cloud Pak® for Data platform, available either as additional features or as built-in data source services, ensuring that nearly all data is accessible across hybrid or multicloud frameworks to support AI-driven applications. You can easily unify your transactional data repositories and swiftly extract insights through intelligent, universal querying across diverse data sources. The multimodel functionality helps reduce expenses by removing the necessity for data replication and migration. Additionally, Db2 offers enhanced flexibility, allowing for deployment on any cloud service provider, which further optimizes operational agility and responsiveness. This versatility in deployment options ensures that businesses can adapt their data management strategies as their needs evolve.

Greenplum Database® stands out as a sophisticated, comprehensive, and open-source data warehouse solution. It excels in providing swift and robust analytics on data volumes that reach petabyte scales. Designed specifically for big data analytics, Greenplum Database is driven by a highly advanced cost-based query optimizer that ensures exceptional performance for analytical queries on extensive data sets. This project operates under the Apache 2 license, and we extend our gratitude to all current contributors while inviting new ones to join our efforts. In the Greenplum Database community, every contribution is valued, regardless of its size, and we actively encourage diverse forms of involvement. This platform serves as an open-source, massively parallel data environment tailored for analytics, machine learning, and artificial intelligence applications. Users can swiftly develop and implement models aimed at tackling complex challenges in fields such as cybersecurity, predictive maintenance, risk management, and fraud detection, among others. Dive into the experience of a fully integrated, feature-rich open-source analytics platform that empowers innovation.

Apache Druid is a distributed data storage solution that is open source. Its fundamental architecture merges concepts from data warehouses, time series databases, and search technologies to deliver a high-performance analytics database capable of handling a diverse array of applications. By integrating the essential features from these three types of systems, Druid optimizes its ingestion process, storage method, querying capabilities, and overall structure. Each column is stored and compressed separately, allowing the system to access only the relevant columns for a specific query, which enhances speed for scans, rankings, and groupings. Additionally, Druid constructs inverted indexes for string data to facilitate rapid searching and filtering. It also includes pre-built connectors for various platforms such as Apache Kafka, HDFS, and AWS S3, as well as stream processors and others. The system adeptly partitions data over time, making queries based on time significantly quicker than those in conventional databases. Users can easily scale resources by simply adding or removing servers, and Druid will manage the rebalancing automatically. Furthermore, its fault-tolerant design ensures resilience by effectively navigating around any server malfunctions that may occur. This combination of features makes Druid a robust choice for organizations seeking efficient and reliable real-time data analytics solutions.

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

Gravity aims to simplify the process of streaming data from over 100 different sources, allowing users to pay only for what they actually utilize. By providing a straightforward interface, Gravity eliminates the need for engineering teams to create streaming pipelines, enabling users to set up streaming from databases, event data, and APIs in just minutes. This empowers everyone on the data team to engage in a user-friendly point-and-click environment, allowing you to concentrate on developing applications, services, and enhancing customer experiences. Additionally, Gravity offers comprehensive execution tracing and detailed error messages for swift problem identification and resolution. To facilitate a quick start, we have introduced various new features, including bulk setup options, predefined schemas, data selection capabilities, and numerous job modes and statuses. With Gravity, you can spend less time managing infrastructure and more time performing data analysis, as our intelligent engine ensures your pipelines run seamlessly. Furthermore, Gravity provides integration with your existing systems for effective notifications and orchestration, enhancing overall workflow efficiency. Ultimately, Gravity equips your team with the tools needed to transform data into actionable insights effortlessly.

AWS Marketplace serves as a carefully organized digital platform that allows users to explore, buy, implement, and oversee third-party software, data products, and services seamlessly within the AWS environment. This marketplace offers a vast array of options spanning various categories, including security, machine learning, business applications, and DevOps tools. By featuring adaptable pricing structures like pay-as-you-go, annual subscriptions, and free trials, AWS Marketplace makes it easier for customers to manage procurement and billing by consolidating expenses into a single AWS invoice. Additionally, it facilitates quick deployment of pre-configured software that can be easily launched on AWS infrastructure. This efficient model not only empowers businesses to spur innovation and reduce time-to-market but also enhances their ability to control software utilization and costs effectively. Ultimately, AWS Marketplace stands as an essential tool for organizations looking to optimize their software management and procurement processes.

Elestio is a comprehensive DevOps platform that allows users to launch over 350 open source software applications on dedicated virtual machines in less than three minutes. This service manages crucial tasks such as installation, configuration, encryption, backups, and both software and OS updates, enabling users to concentrate on maximizing the software's capabilities. Elestio is versatile in deployment options, supporting a variety of cloud providers like DigitalOcean, AWS, VULTR, Hetzner, Linode, and Scaleway, as well as on-premise setups, thereby providing flexibility and minimizing the risk of vendor lock-in. All offerings are powered by dedicated hardware, ensuring users have full access to resources and enhanced kernel-level security. The platform guarantees data protection by encrypting all connections between the user's device, the dashboard, and the services through end-to-end TLS encryption. Additionally, Elestio features a fully managed CI/CD system that integrates seamlessly with GitHub, GitLab, and Docker registries while remaining compatible with any Linux technology stack. This makes it an ideal choice for developers looking for a reliable and secure deployment environment.

An in-memory, column-oriented database combined with a Massively Parallel Processing (MPP) architecture enables the rapid querying of billions of records within mere seconds. The distribution of queries across all nodes in a cluster ensures linear scalability, accommodating a larger number of users and facilitating sophisticated analytics. The integration of MPP, in-memory capabilities, and columnar storage culminates in a database optimized for exceptional data analytics performance. With various deployment options available, including SaaS, cloud, on-premises, and hybrid solutions, data analysis can be performed in any environment. Automatic tuning of queries minimizes maintenance efforts and reduces operational overhead. Additionally, the seamless integration and efficiency of performance provide enhanced capabilities at a significantly lower cost compared to traditional infrastructure. Innovative in-memory query processing has empowered a social networking company to enhance its performance, handling an impressive volume of 10 billion data sets annually. This consolidated data repository, paired with a high-speed engine, accelerates crucial analytics, leading to better patient outcomes and improved financial results for the organization. As a result, businesses can leverage this technology to make quicker data-driven decisions, ultimately driving further success.

Explore a diverse array of SQL features that allow you to build applications ranging from straightforward analytics to complex hybrid transactional and analytical processing. If you're eager to uncover insights from your data, striving for efficiency, or facing tight deadlines, MonetDB can deliver query results in just seconds or even faster. For those looking to leverage or modify their own code and requiring specialized functions, MonetDB provides hooks to integrate user-defined functions in SQL, Python, R, or C/C++. Become part of the vibrant MonetDB community that spans over 130 countries, including students, educators, researchers, startups, small businesses, and large corporations. Embrace the forefront of analytical database technology and ride the wave of innovation! Save time with MonetDB’s straightforward installation process, allowing you to quickly get your database management system operational. This accessibility ensures that users of all backgrounds can efficiently harness the power of data for their projects.

PostgreSQL stands out as a highly capable, open-source object-relational database system that has been actively developed for more than three decades, earning a solid reputation for its reliability, extensive features, and impressive performance. Comprehensive resources for installation and usage are readily available in the official documentation, which serves as an invaluable guide for both new and experienced users. Additionally, the open-source community fosters numerous forums and platforms where individuals can learn about PostgreSQL, understand its functionalities, and explore job opportunities related to it. Engaging with this community can enhance your knowledge and connection to the PostgreSQL ecosystem. Recently, the PostgreSQL Global Development Group announced updates for all supported versions, including 15.1, 14.6, 13.9, 12.13, 11.18, and 10.23, which address 25 reported bugs from the past few months. Notably, this marks the final release for PostgreSQL 10, meaning that it will no longer receive any security patches or bug fixes going forward. Therefore, if you are currently utilizing PostgreSQL 10 in your production environment, it is highly recommended that you plan to upgrade to a more recent version to ensure continued support and security. Upgrading will not only help maintain the integrity of your data but also allow you to take advantage of the latest features and improvements introduced in newer releases.

Presto serves as an open-source distributed SQL query engine designed for executing interactive analytic queries across data sources that can range in size from gigabytes to petabytes. It addresses the challenges faced by data engineers who often navigate multiple query languages and interfaces tied to isolated databases and storage systems. Presto stands out as a quick and dependable solution by offering a unified ANSI SQL interface for comprehensive data analytics and your open lakehouse. Relying on different engines for various workloads often leads to the necessity of re-platforming in the future. However, with Presto, you benefit from a singular, familiar ANSI SQL language and one engine for all your analytic needs, negating the need to transition to another lakehouse engine. Additionally, it efficiently accommodates both interactive and batch workloads, handling small to large datasets and scaling from just a few users to thousands. By providing a straightforward ANSI SQL interface for all your data residing in varied siloed systems, Presto effectively integrates your entire data ecosystem, fostering seamless collaboration and accessibility across platforms. Ultimately, this integration empowers organizations to make more informed decisions based on a comprehensive view of their data landscape.

Apache Kylin™ is a distributed, open-source Analytical Data Warehouse designed for Big Data, aimed at delivering OLAP (Online Analytical Processing) capabilities in the modern big data landscape. By enhancing multi-dimensional cube technology and precalculation methods on platforms like Hadoop and Spark, Kylin maintains a consistent query performance, even as data volumes continue to expand. This innovation reduces query response times from several minutes to just milliseconds, effectively reintroducing online analytics into the realm of big data. Capable of processing over 10 billion rows in under a second, Kylin eliminates the delays previously associated with report generation, facilitating timely decision-making. It seamlessly integrates data stored on Hadoop with popular BI tools such as Tableau, PowerBI/Excel, MSTR, QlikSense, Hue, and SuperSet, significantly accelerating business intelligence operations on Hadoop. As a robust Analytical Data Warehouse, Kylin supports ANSI SQL queries on Hadoop/Spark and encompasses a wide array of ANSI SQL functions. Moreover, Kylin’s architecture allows it to handle thousands of simultaneous interactive queries with minimal resource usage, ensuring efficient analytics even under heavy loads. This efficiency positions Kylin as an essential tool for organizations seeking to leverage their data for strategic insights.

Bid farewell to abandoned data catalogs. Acryl Cloud accelerates time-to-value by implementing Shift Left methodologies for data producers and providing an easy-to-navigate interface for data consumers. It enables the continuous monitoring of data quality incidents in real-time, automating anomaly detection to avert disruptions and facilitating swift resolutions when issues arise. With support for both push-based and pull-based metadata ingestion, Acryl Cloud simplifies maintenance, ensuring that information remains reliable, current, and authoritative. Data should be actionable and operational. Move past mere visibility and leverage automated Metadata Tests to consistently reveal data insights and identify new opportunities for enhancement. Additionally, enhance clarity and speed up resolutions with defined asset ownership, automatic detection, streamlined notifications, and temporal lineage for tracing the origins of issues while fostering a culture of proactive data management.

The intelligent semantic layer merges data with its business context and interconnections, consolidates metrics, and speeds up the production of data products by allowing for SQL queries that are 90% shorter. Users can easily model the data using familiar business terminology, creating a shared understanding and aligning the metrics with business objectives. By defining semantic relationships that replace traditional JOIN operations, queries become significantly more straightforward. Hierarchies and classifications are utilized to enhance data comprehension. The system automatically aligns data with the semantic model, enabling the integration of various data sources through a robust distributed SQL engine that supports large-scale querying. Data can be accessed as an interconnected semantic graph, improving performance while reducing computing expenses through an advanced caching engine and materialized views. Users gain from sophisticated query optimization techniques. Additionally, Timbr allows connectivity to a wide range of cloud services, data lakes, data warehouses, databases, and diverse file formats, ensuring a seamless experience with your data sources. When executing a query, Timbr not only optimizes it but also efficiently delegates the task to the backend for improved processing. This comprehensive approach ensures that users can work with their data more effectively and with greater agility.

Cake AI serves as a robust infrastructure platform designed for teams to effortlessly create and launch AI applications by utilizing a multitude of pre-integrated open source components, ensuring full transparency and governance. It offers a carefully curated, all-encompassing suite of top-tier commercial and open source AI tools that come with ready-made integrations, facilitating the transition of AI applications into production seamlessly. The platform boasts features such as dynamic autoscaling capabilities, extensive security protocols including role-based access and encryption, as well as advanced monitoring tools and adaptable infrastructure that can operate across various settings, from Kubernetes clusters to cloud platforms like AWS. Additionally, its data layer is equipped with essential tools for data ingestion, transformation, and analytics, incorporating technologies such as Airflow, DBT, Prefect, Metabase, and Superset to enhance data management. For effective AI operations, Cake seamlessly connects with model catalogs like Hugging Face and supports versatile workflows through tools such as LangChain and LlamaIndex, allowing teams to customize their processes efficiently. This comprehensive ecosystem empowers organizations to innovate and deploy AI solutions with greater agility and precision.

Oracle's database offerings provide clients with cost-effective and high-efficiency options, including the renowned multi-model database management system, as well as in-memory, NoSQL, and MySQL databases. The Oracle Autonomous Database, which can be accessed on-premises through Oracle Cloud@Customer or within the Oracle Cloud Infrastructure, allows users to streamline their relational database systems and lessen management burdens. By removing the intricacies associated with operating and securing Oracle Database, Oracle Autonomous Database ensures customers experience exceptional performance, scalability, and reliability. Furthermore, organizations concerned about data residency and network latency can opt for on-premises deployment of Oracle Database. Additionally, clients who rely on specific versions of Oracle databases maintain full authority over their operational versions and the timing of any updates. This flexibility empowers businesses to tailor their database environments according to their unique requirements.

Data is essential to improve the efficiency, profitability, and experience of streaming video. Datazoom allows video publishers to manage distributed architectures more efficiently by centralizing, standardizing and integrating data in real time. This creates a more powerful data pipeline, improves observability and adaptability, as well as optimizing solutions. Datazoom is a video data platform which continuously gathers data from endpoints such as a CDN or video player through an ecosystem of collectors. Once the data has been gathered, it is normalized with standardized data definitions. The data is then sent via available connectors to analytics platforms such as Google BigQuery, Google Analytics and Splunk. It can be visualized using tools like Looker or Superset. Datazoom is your key for a more efficient and effective data pipeline. Get the data you need right away. Do not wait to get your data if you have an urgent issue.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.