Best Data Management Software for Presto

DbVisualizer is one of the world’s most popular database editors. Developers, analysts, and DBAs use it to elevate their SQL experience with modern tools to visualize and manage their databases, schemas, objects, and table data, and to auto-generate, write and optimize queries. And so much more. It connects to all popular databases (e.g. MySQL, PostgreSQL, SQL Server, Oracle, Cassandra, Snowflake, SQLite, BigQuery, and 20+ more) and runs on all popular OSes (Windows, macOS, and Linux). With almost 6 million downloads and Pro-users in 145 countries around the world, it won’t let you down. Free and Pro versions are available.

StrongDM is a People-First Access platform that gives technical staff a direct route to the critical infrastructure they need to be their most productive. End users enjoy fast, intuitive, and auditable access to the resources they need, and administrators leverage simplified workflows to enhance security and compliance postures. - We open up a clear, direct path that gives individualized access to the right people and keeps everyone else out. - Total visibility into everything that’s ever happened in your stack. Security and Compliance teams can easily answer who did what, where, and when. - Admins have precise control over what each user has access to—without these controls ever getting in the way of productivity - IT, InfoSec, and Administrators have precise controls. Unauthorized access is eliminated because users never see resources they don’t have permission to use. -All past, present, and future infrastructure is supported - Responsive 24/7/365 customer support.

Free multi-platform database tool for developers, database administrators, analysts and all people who need to work with databases. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, MS Access, Teradata, Firebird, Apache Hive, Phoenix, Presto, etc. Copy As: format configuration editor was added. Extra configuration for filter dialog (performance). Sort by column as fixed (for small fetch sizes). Case-insensitive filters support was added. Plaintext view now support top/bottom dividers. Data editor was fixed (when column name conflicts with alias name). Duplicate row(s) command was fixed for multiple selected rows. Edit sub-menu was returned to the context menu. Columns auto-size configuration was added. Dictionary viewer was fixed (for read-only connections). Current/selected row highlighting support was added (configurable).

StarfishETL is an Integration Platform as a Service (iPaaS), and although “integration” is in the name, it’s capable of much more. An iPaaS lives in the cloud and can integrate different systems by using their APIs. This makes it adaptable beyond integration for migration, data governance, and data cleansing. Unlike traditional integration apps, StarfishETL provides low-code mapping and powerful scripting tools to manage, personalize, and manipulate data at scale. Features: - Drag and drop mapping - AI-powered connections - Purpose built integrations - Extensibility through scripting - Secure on-premises connections - Scalable data capacity

Datameer revolutionizes data transformation with a low-code approach, trusted by top global enterprises. Craft, transform, and publish data seamlessly with no code and SQL, simplifying complex data engineering tasks. Empower your data teams to make informed decisions confidently while saving costs and ensuring responsible self-service analytics. Speed up your analytics workflow by transforming datasets to answer ad-hoc questions and support operational dashboards. Empower everyone on your team with our SQL or Drag-and-Drop to transform your data in an intuitive and collaborative workspace. And best of all, everything happens in Snowflake. Datameer is designed and optimized for Snowflake to reduce data movement and increase platform adoption. Some of the problems Datameer solves: - Analytics is not accessible - Drowning in backlog - Long development

Turn your customers into success stories with built-in intelligence featuring state of the art analytics, dashboards and more. Bold BI is a platform designed from the ground up to be embedded, there are no compromises, no shortcuts. Outdated requirements such as “bring all your data into our data store or cube” today add unnecessary complexity to your application. With some solutions, you must design a complex system for data integration and keep the embedded BI data store in sync with your data sources. With Bold BI, you connect directly to hundreds of data sources and any data warehouse of your choice. Deploy with single-tenant mode if you have a shared data model. If you have an isolated data model, use multi-tenant mode to maintain a single source for dashboards and publish across different tenants. Bold BI is designed to accommodate scenarios common and uncommon. Embed Bold BI directly from your application code using powerful JavaScript APIs.

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

BigLake is a storage engine that unifies data warehouses and lakes by enabling BigQuery and open-source frameworks like Spark to access data with fine-grained access control. BigLake provides accelerated query performance across multi-cloud storage and open formats such as Apache Iceberg. Store a single copy of data with uniform features across data warehouses & lakes. Fine-grained access control and multi-cloud governance over distributed data. Seamless integration with open-source analytics tools and open data formats. Unlock analytics on distributed data regardless of where and how it’s stored, while choosing the best analytics tools, open source or cloud-native over a single copy of data. Fine-grained access control across open source engines like Apache Spark, Presto, and Trino, and open formats such as Parquet. Performant queries over data lakes powered by BigQuery. Integrates with Dataplex to provide management at scale, including logical data organization.

Hue brings the best querying experience with the most intelligent autocomplete and query editor components. The tables and storage browsers leverage your existing data catalog knowledge transparently. Help users find the correct data among thousands of databases and self-document it. Assist users with their SQL queries and leverage rich previews for links, sharing from the editor directly in Slack. Several apps, each one specialized in a certain type of querying are available. Data sources can be explored first via the browsers. The editor shines for SQL queries. It comes with an intelligent autocomplete, risk alerts, and self-service troubleshooting. Dashboards focus on visualizing indexed data but can also query SQL databases. You can now search for certain cell values in the table and the results are highlighted. To make your SQL editing experience, Hue comes with one of the best SQL autocomplete on the planet.

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

Understand how users are interacting with your product and identify opportunity areas to inform your product decisions. Mode empowers one Stitch analyst to do the work of a full data team through speed, flexibility, and collaboration. Build dashboards for annual revenue, then use chart visualizations to identify anomalies quickly. Create polished, investor-ready reports or share analysis with teams for collaboration. Connect your entire tech stack to Mode and identify upstream issues to improve performance. Speed up workflows across teams with APIs and webhooks. Understand how users are interacting with your product and identify opportunity areas to inform your product decisions. Leverage marketing and product data to fix weak spots in your funnel, improve landing-page performance, and understand churn before it happens.

Whether you want to build a governed delta lake, data warehouse, or simply want to migrate from your traditional database to a modern cloud data warehouse, do it all with Lyftrondata. Simply create and manage all of your data workloads on one platform by automatically building your pipeline and warehouse. Analyze it instantly with ANSI SQL, BI/ML tools, and share it without worrying about writing any custom code. Boost the productivity of your data professionals and shorten your time to value. Define, categorize, and find all data sets in one place. Share these data sets with other experts with zero codings and drive data-driven insights. This data sharing ability is perfect for companies that want to store their data once, share it with other experts, and use it multiple times, now and in the future. Define dataset, apply SQL transformations or simply migrate your SQL data processing logic to any cloud data warehouse.

The only fully managed cloud data lakehouse designed to ingest from all your data sources in minutes and support all your query engines at scale, for a fraction of the cost. Ingest from databases and event streams at TB-scale in near real-time, with the simplicity of fully managed pipelines. Query your data with any engine, and support all your use cases including BI, real-time analytics, and AI/ML. Cut your costs by 50% or more compared to cloud data warehouses and ETL tools with simple usage-based pricing. Deploy in minutes without engineering overhead with a fully managed, highly optimized cloud service. Unify your data in a single source of truth and eliminate the need to copy data across data warehouses and lakes. Use the right table format for the job, with omnidirectional interoperability between Apache Hudi, Apache Iceberg, and Delta Lake. Quickly configure managed pipelines for database CDC and streaming ingestion.

lakeFS enables you to manage your data lake the way you manage your code. Run parallel pipelines for experimentation and CI/CD for your data. Simplifying the lives of engineers, data scientists and analysts who are transforming the world with data. lakeFS is an open source platform that delivers resilience and manageability to object-storage based data lakes. With lakeFS you can build repeatable, atomic and versioned data lake operations, from complex ETL jobs to data science and analytics. lakeFS supports AWS S3, Azure Blob Storage and Google Cloud Storage (GCS) as its underlying storage service. It is API compatible with S3 and works seamlessly with all modern data frameworks such as Spark, Hive, AWS Athena, Presto, etc. lakeFS provides a Git-like branching and committing model that scales to exabytes of data by utilizing S3, GCS, or Azure Blob for storage.

Hudi is a rich platform to build streaming data lakes with incremental data pipelines on a self-managing database layer, while being optimized for lake engines and regular batch processing. Hudi maintains a timeline of all actions performed on the table at different instants of time that helps provide instantaneous views of the table, while also efficiently supporting retrieval of data in the order of arrival. A Hudi instant consists of the following components. Hudi provides efficient upserts, by mapping a given hoodie key consistently to a file id, via an indexing mechanism. This mapping between record key and file group/file id, never changes once the first version of a record has been written to a file. In short, the mapped file group contains all versions of a group of records.