Presto Integrations

PopSQL is the evolution of legacy SQL editors like DataGrip, DBeaver, Postico. We provide a beautiful, modern SQL editor for data focused teams looking to save time, improve data accuracy, onboard new hires faster, and deliver insights to the business fast. With PopSQL, users can easily understand their data model, write version controlled SQL, collaborate with live presence, visualize data in charts and dashboards, schedule reports, share results, and organize foundational queries for search and discovery. Even if your team is already leveraging a large BI tool, like Tableau or Looker, or a hodge podge of SQL editors, PopSQL enables seamless collaboration between your SQL power users, junior analysts, and even your less technical stakeholders who are hungry for data insights. * Cross-platform compatibility with macOS, Windows, and Linux * Works with Snowflake, Redshift, BigQuery, Clickhouse, Databricks, Athena, MongoDB, PostgreSQL, MySQL, SQL Server, SQLite, Presto, Cassandra, and more

You can quickly find answers with the flexible visual query builder. You can access data via your browser or phone and analyze it anywhere you are. No SQL, data scientist, or desktop required. You can learn from your team mates and search for any questions within your organization with shared questions. @mentions, push notifications and shared chat allow you to bring the right people in the conversation and make data actionable. You can easily copy and modify shared questions, export data and change the way charts are displayed so you don't just see someone else's analysis but make it yours. External sharing can be turned on to allow access to data tables and partners outside your domain. In just two clicks, you can access the underlying data tables. Smart typeaheads make it easy to run custom SQL.

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

Made by industry experts. Tested at the largest tech companies in the world. Connect your people and connect your data. Every employee can access data. Reduce costs by retiring on-prem infrastructure. Multiply the productivity of your data team. We have a long history supporting open source projects and technical communities. We value the energy, open standards and exchange of ideas that result from passionate professionals coming together for a common cause. Noteable is committed to supporting technical communities, and contributing to open source whenever possible. Noteable is your data platform. It transforms the way data teams work by enabling modern collaboration securely and co-operatively among all your users. You can deploy to a multi-tenant cloud, or a single-tenant virtual-private cluster. You have complete control over the location, network setup, and other details. You set all rules for your cloud.

DQOps is a data quality monitoring platform for data teams that helps detect and address quality issues before they impact your business. Track data quality KPIs on data quality dashboards and reach a 100% data quality score. DQOps helps monitor data warehouses and data lakes on the most popular data platforms. DQOps offers a built-in list of predefined data quality checks verifying key data quality dimensions. The extensibility of the platform allows you to modify existing checks or add custom, business-specific checks as needed. The DQOps platform easily integrates with DevOps environments and allows data quality definitions to be stored in a source repository along with the data pipeline code.

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

Our platform allows businesses to use data, including its application in advanced analysis, machine learning and AI, to do great things without worrying that customers, employees or intellectual property are at risk. The Protegrity Data Protection Platform does more than just protect data. It also classifies and discovers data, while protecting it. It is impossible to protect data you don't already know about. Our platform first categorizes data, allowing users the ability to classify the type of data that is most commonly in the public domain. Once those classifications are established, the platform uses machine learning algorithms to find that type of data. The platform uses classification and discovery to find the data that must be protected. The platform protects data behind many operational systems that are essential to business operations. It also provides privacy options such as tokenizing, encryption, and privacy methods.

Tableau Catalog is a benefit for everyone. Tableau Catalog provides a complete view of the data and how it connects to the analytics in Tableau. This increases trust and discoverability for IT and business users. Tableau Catalog makes it easy to communicate changes to the data, review dashboards, or search for the right data for analysis. Tableau Catalog automatically ingests all data assets in your Tableau environment into a single central list. There is no need to create an index schedule or connect. You can quickly see all of your files, tables, and databases in one location. Migration of databases, deprecating fields or adding a column to a table can all have potential impacts on your environment. Lineage and impact analysis allows you to see not only the upstream and downstream implications of assets but also who will be affected.

You can quickly create and share dynamic, customizable, and beautiful dashboards that showcase your data in just a few clicks. Explore your data with our no-code visualiser or perform deeper analysis using the state-of-the art SQL editor. A lightweight, powerful visualization layer will allow you to leverage the investments made in your data infrastructure. Superset doesn't require any additional ingestion layers and is independent of your underlying data architecture. Apache Superset is an open-source data visualization tool that was developed out of Airbnb. Preset was founded by the original creator and maintainer of Superset. It provides a complete, easy-to-use, enterprise-ready platform for Superset.

Anomalo helps you get ahead of data issues by automatically detecting them as soon as they appear and before anyone else is impacted. -Depth of Checks: Provides both foundational observability (automated checks for data freshness, volume, schema changes) and deep data quality monitoring (automated checks for data consistency and correctness). -Automation: Use unsupervised machine learning to automatically identify missing and anomalous data. -Easy for everyone, no-code UI: A user can generate a no-code check that calculates a metric, plots it over time, generates a time series model, sends intuitive alerts to tools like Slack, and returns a root cause analysis. -Intelligent Alerting: Incredibly powerful unsupervised machine learning intelligently readjusts time series models and uses automatic secondary checks to weed out false positives. -Time to Resolution: Automatically generates a root cause analysis that saves users time determining why an anomaly is occurring. Our triage feature orchestrates a resolution workflow and can integrate with many remediation steps, like ticketing systems. -In-VPC Development: Data never leaves the customer’s environment. Anomalo can be run entirely in-VPC for the utmost in privacy & security

Rakam offers verticalized reporting capabilities for different teams' reporting needs. Rakam converts the questions you ask in the user interface to SQL, hiding the complexity for the end-users. rakam does not load data into your data warehouse. It expects you have all data in your data-warehouse. This allows you to analyze the data without having to pull data from outside your data warehouse. This blog post is about the topic. Rakam supports dbt core natively as the data modeling layer. rakam does not run your dbt transforms. Instead, it connects to your GIT repository and synchronizes all your dbt model automatically. rakam can create incremental DBT models. Make use of them to speed your queries and reduce your query costs. rakam creates programmatic roll-up models by hiding the complexity of aggregates in your dbt resources files.

A streaming database is designed to store, process, analyze, and ingest large data streams. It is a modern data infrastructure which unifies messaging, stream processing and storage to help you get the most out of your data in real time. Massive amounts of data are continuously ingested from many sources, including IoT device sensor sensors. A specially designed distributed streaming data storage cluster can store millions of data streams securely. Subscribe to HStreamDB topics to access data streams in real time as fast as Kafka. You can access and playback data streams at any time thanks to the permanent stream storage. Data streams can be processed based on event-time using the same SQL syntax that you use to query relational databases. SQL can be used to filter, transform and aggregate multiple data streams.

The toolkit to build interactive, fully customized analytics experiences into your apps. Embeddable believes that you shouldn't be forced to choose between buying or building your analytics solution. While creating charts, graphs and dashboards is an expensive, ongoing commitment, the out-of-the box solutions do not deliver the user experience that you desire for your clients. Welcome to the world of limitless creativity where you can create analytics experiences that are truly remarkable, and surpass your customers' expectations. Create your perfect experience using best-in class open source libraries. Embeddable displays data in your app using a secure read only transaction, regardless of whether your data is stored in a central data warehouse or distributed across multiple microservices. Let your imagination run wild and create the analytics solution you want, without compromising.

Connect and centralize data. Transform your data landscape into a flexible powerhouse. Data Virtuality is a data integration platform that allows for instant data access, data centralization, and data governance. Logical Data Warehouse combines materialization and virtualization to provide the best performance. For high data quality, governance, and speed-to-market, create your single source data truth by adding a virtual layer to your existing data environment. Hosted on-premises or in the cloud. Data Virtuality offers three modules: Pipes Professional, Pipes Professional, or Logical Data Warehouse. You can cut down on development time up to 80% Access any data in seconds and automate data workflows with SQL. Rapid BI Prototyping allows for a significantly faster time to market. Data quality is essential for consistent, accurate, and complete data. Metadata repositories can be used to improve master data management.

Astronomer is the driving force behind Apache Airflow, the de facto standard for expressing data flows as code. Airflow is downloaded more than 4 million times each month and is used by hundreds of thousands of teams around the world. For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Founded in 2018, Astronomer is a global remote-first company with hubs in Cincinnati, New York, San Francisco, and San Jose. Customers in more than 35 countries trust Astronomer as their partner for data orchestration.

Multi-cloud data security with a single pane of glass Industry's first SaaS access governance solution. Cloud is fragmented and data is scattered across different systems. Sensitive data is difficult to access and control due to limited visibility. Complex data onboarding hinders data scientist productivity. Data governance across services can be manual and fragmented. It can be time-consuming to securely move data to the cloud. Maximize visibility and assess the risk of sensitive data distributed across multiple cloud service providers. One system that enables you to manage multiple cloud services' data policies in a single place. Support RTBF, GDPR and other compliance requests across multiple cloud service providers. Securely move data to the cloud and enable Apache Ranger compliance policies. It is easier and quicker to transform sensitive data across multiple cloud databases and analytical platforms using one integrated system.

Complexity is the enemy of security. Simplify and scale fine-grained data access control. Dynamically authorize and audit every query to comply with data security and privacy regulations. Okera integrates seamlessly into your infrastructure – in the cloud, on premise, and with cloud-native and legacy tools. With Okera, data users can use data responsibly, while protecting them from inappropriately accessing data that is confidential, personally identifiable, or regulated. Okera’s robust audit capabilities and data usage intelligence deliver the real-time and historical information that data security, compliance, and data delivery teams need to respond quickly to incidents, optimize processes, and analyze the performance of enterprise data initiatives.

Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.

Bigeye is a data observability platform that allows teams to measure, improve and communicate data quality at any scale. A data quality problem can cause an outage that causes trust in the data. Bigeye starts with monitoring to rebuild trust. Before executives see it in a dashboard, find missing or broken reporting data. Before models are retrained, be aware of potential issues in training data. You need to get rid of that uncomfortable feeling that most data is correct most of the time. The status of a pipeline job doesn't tell the entire story. Monitoring the actual data is the best way to make sure data is available for use. Monitoring data-level freshness will ensure that pipelines run on schedule even when ETL orchestrators are down. Learn about any changes in event names, region codes or product types and other categorical data. To ensure that everything is working as it should, detect drops or spikes of row counts, nulls, or blank values.

Hex combines the best of notebooks and BI into a seamless, collaborative interface. Hex is a modern Data Workspace. It makes it easy for you to connect to data and analyze it in collaborative SQL or Python-powered notebooks. You can also share work as interactive data apps or stories. The Projects page is your default landing page in Hex. You can quickly find the projects you have created and those you share with others. The outline gives you an easy-to-read overview of all cells in a project's Logic View. Each cell in the outline lists all variables it defines and any cells that return an output (chart cells or Input Parameters cells, etc.). Display a preview of the output. To jump to a specific position in the logic, you can click on any cell in the outline.

Data-driven decision making has led to a surge in business data and an increase in demand for its analysis. IT departments are now looking to move away from expensive Enterprise Data Warehouses (EDW), and towards more cost-effective Big Data platforms such as Hadoop or AWS. The Total Cost of Ownership (TCO), for these new platforms, is approximately 10 times lower. They are not suitable for interactive BI applications as they lack the same performance and user concurrency as legacy EDWs. Jethro was created precisely for this purpose. Customers use Jethro to perform interactive BI with Big Data. Jethro is a transparent middle-tier that does not require any changes to existing apps and data. It is self-driving and requires no maintenance. Jethro is compatible to BI tools such as Microstrategy, Qlik and Tableau and is data source agnostic. Jethro meets the needs of business users by allowing thousands of concurrent users to run complex queries across billions of records.

AI can be used to generate, explain, and optimize SQL and NoSQL queries. Improve your SQL productivity, regardless of your experience. Connect to your data source, and retrieve data insights with ease. This service allows you to generate SQL queries within seconds. This is a game changer for those who work with large databases constantly and need quick results. The service is very affordable. This is a cost-effective service for any data analyst. It's an investment that will save you countless work hours. This service will boost your SQL proficiency and productivity, whether you're an experienced data analyst or a novice. It is designed to be easy to use and accessible for all levels of expertise. Our AI not only creates SQL queries, but also explains them and optimizes the results.

Discover the nuance of trends by following a journey of guided data analytics questions. Self-service data analysis empowers your stakeholders. Give them the power to access and analyse the data they require, whenever they need it. Self-service analytics solutions can increase efficiency, reduce IT dependency, and help you make better data-driven decisions. Athenic AI can answer your questions by connecting to data in a database or data warehouse, or even an application such as a CRM or ERP platform. You don't need to know SQL or to hire a business analyst. Athenic is built to be able translate natural language into SQL queries. We've even added the ability to give the AI context in natural languages.

Sugar will charge fees based on the organization. Multiple users can be part of an organization. Under an organization, multiple spaces can be created. In general, it is best to divide spaces into projects or teams. Data is not shared between spaces. Each space has its independent permission management. Sugar requires that you specify the original data source when you analyze and visualize data. The data source is where the data is stored. It is usually the connection address (host name, port number, user name, and password). The database. Dashboards are visual pages that have a cool visual effect. They are usually used on a large screen to visualize real-time data.

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

This is the place where everyone can ask questions and get data-based advice. Get your data connected and in front of your employees. Dashboards, such as this one, are easy to create, share, and examine. Any member of your team can access answers to data questions with just a few clicks. This includes the CEO and Customer Support. SQL and our notebook editor can be used to simplify complex questions. Visual joins, multiple aggregates and filtering steps allow you to dig deeper into your data. To create interactive visualizations that users can modify and explore, you can add variables to your queries. To get the right data to the right people at the right moment, set up alerts or scheduled reports. You can either use the hosted version or Docker to get started on your own. Connect to your existing data and invite your team. You have a BI solution that will take less than a sales call.

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

Automate the generation of native Google Slide and PowerPoint presentations using customized data inputs. You can create personalized content for all your customers, not just a few. Make sure that every presentation adheres to your brand guidelines. Your audience will be impressed by your consistent branding across all presentations. You can easily incorporate data from multiple data sources into your templates. To infuse data-driven insights into your templates, automatically extract data from your CRM, dashboards, and other databases. Your sales reps spend hours creating presentations for prospects and existing clients. Give them the freedom to sell and not create presentations. Your reps don’t have to decide which clients should receive a business review/QBR. You can create a customized presentation for each client in minutes, rather than hours. No more putting together pricing proposals manually. Matik will take care of the rest.

Superset is lightweight, fast, intuitive, and loaded full of options that make it easy to explore and visualize data. This includes simple line charts and detailed geospatial maps. Superset can connect through SQLAlchemy to any SQL-based datasource, including modern cloud native databases or engines at petabytescale. Superset is lightweight, highly scalable and can leverage the power of your existing data infrastructure.

SQL is a domain-specific programming language that allows you to access, manage, and manipulate relational databases and relational management systems.

Automate the automatic coverage of thousands of tables using ML-based anomaly detection. 50+ custom metrics are also available. Monitoring of metadata and data. Comprehensive mapping of all dependencies between assets from ingestion to reporting. Collaboration between data consumers and data engineers is enhanced and productivity is increased. Sifflet integrates seamlessly with your data sources and preferred tools. It can run on AWS and Google Cloud Platform as well as Microsoft Azure. Keep an eye on your data's health and notify the team if quality criteria are not being met. In a matter of seconds, you can set up the basic coverage of all your tables. You can set the frequency, criticality, and even custom notifications. Use ML-based rules for any anomaly in your data. There is no need to create a new configuration. Each rule is unique because it learns from historical data as well as user feedback. A library of 50+ templates can be used to complement the automated rules.

No more data catalog ghost cities. Acryl Cloud accelerates time-to-value for data producers through Shift Left practices and an intuitive user interface for data consumers. Continuously detect data-quality incidents in real time, automate anomaly detecting to prevent breakdowns, and drive quick resolution when they occur. Acryl Cloud supports both pull-based and push-based metadata ingestion to ensure information is reliable, current, and definitive. Data should be operational. Automated Metadata Tests can be used to uncover new insights and areas for improvement. They go beyond simple visibility. Reduce confusion and speed up resolution with clear asset ownership and automatic detection. Streamlined alerts and time-based traceability are also available.

Our mission is to make online communication with customers quick and personalized throughout the entire customer journey. Ask Copilot to generate reports in seconds for your business. No need to learn a new data visualization tool. Forecasting sales and demand will help you stay ahead of your competition. Stay on top of data anomalies to gain insights into your customers' needs. Connect your business data to AI Copilot and use it to make data-driven decision. Our platform was born out of necessity and has evolved into a solution that helps startups and small business connect with their audience, nurture lead generation, and ultimately thrive.

Shift testing to one side and identify issues earlier when they are simpler and more cost-effective. Developers can do better integration testing, reduce dev cycles, and increase productivity. More reliable products will be possible with shorter and more thorough integration feedback cycles. Testcontainers Cloud allows developers to run reliable integration testing, with real dependencies defined and coded, from their laptops to the CI of their team. Testcontainers is an Open-Source framework that provides lightweight, throwaway instances of databases, message brokers and web browsers. It can also run just about any other application that can be run in a Docker Container. No need to create mocks or configure complex environments. Simply define your test dependencies in code and run your tests. Containers will be created and deleted.