Alternatives to Mitzu

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.

Warehouse-Native Customer Journey Analytics—No Black Boxes. No Limits. Kubit is the leading customer journey analytics platform, built for product, data, and marketing teams who need self-service insights, real-time visibility, and full control of their data—all without engineering dependencies or vendor lock-in. Unlike traditional analytics tools, Kubit is warehouse-native, enabling you to analyze user behavior directly in your cloud data platform (Snowflake, BigQuery, or Databricks). No data extraction. No hidden algorithms. No black-box logic. With built-in support for funnel analysis, retention, user paths, and cohort exploration, Kubit makes it easy to understand what’s working—and what’s not—across the entire customer journey. Add real-time anomaly detection and exploratory analytics, and you get faster decisions, smarter optimizations, and more engaged users. Top enterprises like Paramount, TelevisaUnivision, and Miro trust Kubit for its flexibility, data governance, and unmatched customer support. Discover the future of customer analytics at kubit.ai

Compass is a data assistant that leverages AI and integrates seamlessly with Slack, enabling users to transform straightforward inquiries into immediate answers, summaries, charts, and insights derived from the actual data in their warehouses. This tool is designed to empower teams to make informed, data-driven choices without having to deal with the delays of BI backlogs or the need to create dashboards beforehand. By establishing direct connections with prominent data warehouses such as Snowflake, BigQuery, Redshift, Postgres, AWS Athena, and Databricks, Compass not only learns the schema and context of your data but also delivers governed, SQL-powered responses and visualizations within the familiar tools used by your team, ensuring data remains secure and under your control. Over time, Compass enhances organizational knowledge, making answers progressively more precise and pertinent, while fostering collaboration through Slack threads, allowing the scheduling of recurring analyses, and maintaining a centralized repository of definitions and insights that diminish analytical silos and lessen dependency on specialized SQL expertise. Furthermore, this innovative solution streamlines the decision-making process, making it easier for teams to access and utilize data effectively.

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.

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.

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.

Nao is an innovative data IDE powered by artificial intelligence, specifically tailored for data teams, seamlessly merging a code editor with direct access to your data warehouse, enabling you to write, test, and manage data-related code while retaining complete contextual awareness. It is compatible with various data warehouses, including Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, and Redshift. Upon connection, nao enhances the conventional data warehouse console by providing features like schema-aware SQL auto-completion, data previews, SQL worksheets, and effortless navigation between multiple warehouses. At the heart of nao lies its intelligent AI agent, which possesses comprehensive knowledge of your data schema, tables, columns, metadata, as well as your codebase or data-stack context. This agent is capable of generating SQL queries, constructing entire data transformation models such as those used in dbt workflows, refactoring existing code, updating documentation, conducting data quality assessments, and performing data-diff tests. Furthermore, it can uncover insights and facilitate exploratory analytics, all while maintaining strict adherence to data structure and quality standards. With its robust capabilities, nao empowers data teams to streamline their workflows and enhance productivity significantly.

Effortlessly import marketing data into your preferred data warehouse or cloud storage solution, including BigQuery, Snowflake, Redshift, Azure SQL, AWS S3, Azure Data Lake, and Google Cloud Storage, through our fully-managed ETL pipelines hosted in the cloud. Our comprehensive marketing data warehouse consolidates all your marketing information and delivers valuable insights, such as advertising performance, cross-channel attribution, content analysis, competitor intelligence, and much more. Additionally, our customer data platform facilitates real-time identity resolution across various data sources, providing a cohesive view of the customer and their journey. Electrik.AI serves as a cloud-driven marketing analytics software and an all-encompassing service platform designed to optimize your marketing efforts. Moreover, Electrik.AI’s Google Analytics Hit Data Extractor is capable of enhancing and retrieving the un-sampled hit-level data transmitted to Google Analytics from your website or application, routinely transferring it to your specified destination database, data warehouse, or data lake for further analysis. This ensures you have access to the most accurate and actionable data to drive your marketing strategies effectively.

Glazed Analytics is an innovative product analytics solution that leverages artificial intelligence to convert Figma design interfaces into actionable tracking plans. By analyzing design screens and integrating your existing event taxonomy, it offers suggestions for pertinent tracking events that users can choose to approve, modify, or decline. This tool generates prompts that are compatible with AI coding assistants, facilitating the deployment of feature tracking without needing extensive developer involvement. Additionally, the platform seamlessly incorporates visual documentation within Figma, embedding tracking specifications into design elements to serve as a centralized source of truth, effectively replacing scattered spreadsheets or Confluence documents. When connected to data warehouses like BigQuery, Redshift, and Snowflake, as well as analytics tools such as Amplitude, Mixpanel, and PostHog, Glazed delivers quality assurance views that highlight any missing or faulty event properties while providing first-seen and volume metrics. It also includes features that automatically check for duplicate naming and allows users to sync the history of design versions, ensuring a streamlined and efficient workflow throughout the product development process. This comprehensive approach not only enhances team collaboration but also optimizes the overall analytics pipeline.

Agile Data Engine serves as a robust DataOps platform crafted to optimize the lifecycle of cloud-based data warehouses, encompassing their development, deployment, and management. This solution consolidates data modeling, transformation processes, continuous deployment, workflow orchestration, monitoring, and API integration into a unified SaaS offering. By leveraging a metadata-driven model, it automates the generation of SQL scripts and the workflows for data loading, significantly boosting efficiency and responsiveness in data operations. The platform accommodates a variety of cloud database systems such as Snowflake, Databricks SQL, Amazon Redshift, Microsoft Fabric (Warehouse), Azure Synapse SQL, Azure SQL Database, and Google BigQuery, thus providing considerable flexibility across different cloud infrastructures. Furthermore, its modular data product architecture and pre-built CI/CD pipelines ensure smooth integration and facilitate ongoing delivery, empowering data teams to quickly adjust to evolving business demands. Additionally, Agile Data Engine offers valuable insights and performance metrics related to the data platform, enhancing overall operational transparency and effectiveness. This capability allows organizations to make informed decisions based on real-time data analytics, further driving strategic initiatives.

AnswerDock is an innovative analytics platform powered by AI, designed specifically for enterprise use. It enables business users to obtain answers to their inquiries and facilitates quicker, more informed decision-making without relying on data analysts. Users can gain immediate insights from their data warehouses through live queries, compatible with platforms like Snowflake, Amazon Redshift, Microsoft Synapse, and Google BigQuery. Additionally, it allows for the uploading of Excel files and connections to traditional relational databases such as MySQL and SQL Server, along with third-party APIs like Google Analytics. You can explore AnswerDock using a sample retail dataset without the hassle of registration or login. For those interested in using their own data, signing up for the free version provides access to all features. With AnswerDock, business users can effortlessly generate their own reports and dashboards by simply entering their questions, similar to how one would use a web search engine. For instance, if you need a sales report, just type in "Top 10 Sales People by growth in number of leads this quarter," and AnswerDock will conduct the analysis and present the best visualization in an instant, making the process incredibly straightforward and user-friendly. This platform is revolutionizing the way companies interact with their data, empowering them to make data-driven decisions with ease.

Trino is a remarkably fast query engine designed to operate at exceptional speeds. It serves as a high-performance, distributed SQL query engine tailored for big data analytics, enabling users to delve into their vast data environments. Constructed for optimal efficiency, Trino excels in low-latency analytics and is extensively utilized by some of the largest enterprises globally to perform queries on exabyte-scale data lakes and enormous data warehouses. It accommodates a variety of scenarios, including interactive ad-hoc analytics, extensive batch queries spanning several hours, and high-throughput applications that require rapid sub-second query responses. Trino adheres to ANSI SQL standards, making it compatible with popular business intelligence tools like R, Tableau, Power BI, and Superset. Moreover, it allows direct querying of data from various sources such as Hadoop, S3, Cassandra, and MySQL, eliminating the need for cumbersome, time-consuming, and error-prone data copying processes. This capability empowers users to access and analyze data from multiple systems seamlessly within a single query. Such versatility makes Trino a powerful asset in today's data-driven landscape.

Uncover the intricacies behind emerging trends by embarking on a guided exploration of data analytics inquiries that reveal the underlying dynamics at play. Enable your stakeholders to harness the power of self-service data analytics, granting them the capability to retrieve and examine the data they require, precisely when they need it. This approach enhances efficiency, diminishes reliance on IT support, and accelerates the process of making informed, data-driven decisions through a self-service analytics platform. Athenic AI seamlessly integrates with your data, whether it is housed in a database, data warehouse, or applications like CRM or ERP systems, providing answers to your queries without necessitating expertise in SQL or the involvement of a business analyst. Designed to comprehend natural language, Athenic translates your inquiries into SQL queries effortlessly. Moreover, we have incorporated a feature that allows users to provide additional context in natural language, further enriching the interaction and insights derived from the data. This empowers users to gain a deeper understanding of their data landscape, fostering a culture of analytical thinking across the organization.

Tokern offers an open-source suite designed for data governance, specifically tailored for databases and data lakes. This user-friendly toolkit facilitates the collection, organization, and analysis of metadata from data lakes, allowing users to execute quick tasks via a command-line application or run it as a service for ongoing metadata collection. Users can delve into aspects like data lineage, access controls, and personally identifiable information (PII) datasets, utilizing reporting dashboards or Jupyter notebooks for programmatic analysis. As a comprehensive solution, Tokern aims to enhance your data's return on investment, ensure compliance with regulations such as HIPAA, CCPA, and GDPR, and safeguard sensitive information against insider threats seamlessly. It provides centralized management for metadata related to users, datasets, and jobs, which supports various other data governance functionalities. With the capability to track Column Level Data Lineage for platforms like Snowflake, AWS Redshift, and BigQuery, users can construct lineage from query histories or ETL scripts. Additionally, lineage exploration can be achieved through interactive graphs or programmatically via APIs or SDKs, offering a versatile approach to understanding data flow. Overall, Tokern empowers organizations to maintain robust data governance while navigating complex regulatory landscapes.

Rakam offers tailored reporting capabilities for various teams, ensuring that no group is confined to a single interface. It seamlessly converts the inquiries made in its user interface into SQL queries, simplifying the process for end-users. Importantly, Rakam does not transfer any data into your data warehouse; rather, it operates under the assumption that all necessary data is already stored within, allowing for analysis directly from the data warehouse, your definitive source of truth. For further insights on this subject, check out our blog post. Rakam also integrates with dbt core, serving as the data modeling layer but does not execute your dbt transformations. Instead, it connects to your GIT repository to automatically synchronize your dbt models. Additionally, Rakam can generate incremental dbt models, enhancing query performance and minimizing database costs. By defining aggregates in your dbt resource files, Rakam automatically creates roll-up models, simplifying the process for end-users while ensuring efficient data handling. This streamlined approach empowers teams to focus on insights rather than the technical intricacies of data analysis.

AskEnola is an advanced analytical tool driven by artificial intelligence that supersedes traditional dashboards and manual reporting processes. It seamlessly integrates with your data warehouse solutions like Snowflake, BigQuery, or Redshift, and comprehends your vital business KPIs to provide immediate answers to intricate inquiries in straightforward language. In contrast to conventional BI tools that require extensive setup, Enola is operational from the start, revealing trends, diagnosing underlying issues, and forecasting results in real-time. Various teams, including those in RevOps, BizOps, FP&A, and Product, leverage Enola to accelerate decision-making, reduce reporting expenses, and enhance the adoption of analytics. By integrating BADIR™ methodology with contextual AI, it effectively dismantles data silos and revolutionizes how businesses respond to market fluctuations. No matter if you're evaluating churn, predicting revenue, or overseeing performance metrics, Enola delivers reliable insights without the need for tickets or dashboards. Experience the future of analytics by requesting your free demo now or see Enola in action through our Sandbox.

In order to get a complete view of customer/account-level journeys, understand attribution, and uncover cross-functional business insights, event data is often duplicated and exported from product analytics solutions to the data warehouse. This creates inconsistent data between two platforms: siloed product analytics solutions and SQL/BI tools running on the data warehouse. Adding to the challenge, BI tools are not even designed to explore and derive insights from event data. NetSpring offers a single self-service tool for product analytics with BI-style ad hoc visual exploration, working directly off the data warehouse as the single source of truth. Key Benefits: - GTM Teams: Self-serve answers to the next business question without worrying about data availability - Data & Analytics Teams: Support GTM teams with governed, self-service tooling - C-Suite: Leverage the data warehouse (source of truth) for consistent results and to avoid data duplication, reverse ETL, and security issues Key Capabilities: - Self-Service: Rich library of behavioral analytics templates - Analytical Power of BI: Self-guided ad hoc visual exploration - Warehouse-Native: Rich business context with no data duplication

Etleap was created on AWS to support Redshift, snowflake and S3/Glue data warehouses and data lakes. Their solution simplifies and automates ETL through fully-managed ETL as-a-service. Etleap's data wrangler allows users to control how data is transformed for analysis without having to write any code. Etleap monitors and maintains data pipes for availability and completeness. This eliminates the need for constant maintenance and centralizes data sourced from 50+ sources and silos into your database warehouse or data lake.

Dataplex Universal Catalog provides enterprise-wide visibility and governance for structured, semi-structured, and unstructured data. Its AI-powered semantic search allows users to query data in natural language, eliminating the need for complex search syntax. The platform enriches metadata with business context through glossaries, ownership attributes, and key usage details, supporting informed decision-making. It offers automated metadata ingestion, classification, and enrichment, reducing manual data management tasks. With built-in lineage tracking, organizations can trace data origins, transformations, and dependencies across multiple sources. BigQuery integration brings these governance capabilities directly into the analytics workflow, enhancing productivity. By connecting with BigLake, Dataplex extends governance to open lakehouses with Apache Iceberg and other engines. The result is a secure, scalable foundation for managing data-to-AI lifecycles across cloud-native and open-source ecosystems.

Dot serves as an AI-driven data analyst, seamlessly linking to your data warehouse and empowering users to pose questions in natural language for immediate, reliable insights. It functions across platforms like Slack, Teams, or through a dedicated web application to facilitate on-demand data retrieval, visualizations, root-cause analyses, and weekly business summaries that come with actionable suggestions. By leveraging existing business intelligence tools, dbt metrics, LookML, SQL queries, and documentation, GetDot.ai guarantees consistent and governed responses supported by role-specific permissions and row-level security measures. The setup process is entirely code-free, featuring one-click integrations for popular SQL-based sources including Snowflake, BigQuery, Redshift, and PostgreSQL. Its continuous monitoring feature reveals previously unknown insights, while a specialized training and governance workspace allows you to fine-tune its functionality and uphold accuracy. Built for efficiency and ease of use, Dot eliminates the clutter of multiple dashboards by providing exact answers in mere seconds, transforming the way data is accessed and utilized. Moreover, this innovative tool not only enhances productivity but also empowers users to make data-driven decisions with confidence.

Salesforce Data 360 is a real-time enterprise data engine designed to transform disconnected data into actionable intelligence. It unifies customer and operational data from multiple systems into a comprehensive business view. Using Zero-Copy architecture, organizations can activate live data directly from their existing warehouses without duplication. The platform supports both structured and unstructured data, including text, images, and streaming events. Identity resolution and data harmonization tools create consistent, reliable customer profiles. Governance features enforce privacy policies and compliance rules automatically. Data 360 enables dynamic audience segmentation and predictive modeling for smarter decision-making. Teams can trigger automated workflows based on real-time data changes. Insights can be shared securely with marketing platforms, analytics tools, and data warehouses. Data 360 empowers enterprises to activate trusted data across every channel and department.

Adaptive Data Virtualization™ technology empowers businesses to operate their current applications on contemporary cloud data warehouses without the need for extensive modifications or reconfiguration. With Datometry Hyper-Q™, organizations can swiftly embrace new cloud databases, effectively manage ongoing operational costs, and enhance their analytical capabilities to accelerate digital transformation efforts. This virtualization software from Datometry enables any existing application to function on any cloud database, thus facilitating interoperability between applications and databases. Consequently, enterprises can select their preferred cloud database without the necessity of dismantling, rewriting, or replacing their existing applications. Furthermore, it ensures runtime application compatibility by transforming and emulating legacy data warehouse functionalities. This solution can be deployed seamlessly on major cloud platforms like Azure, AWS, and GCP. Additionally, applications can leverage existing JDBC, ODBC, and native connectors without any alterations, ensuring a smooth transition. It also establishes connections with leading cloud data warehouses, including Azure Synapse Analytics, AWS Redshift, and Google BigQuery, broadening the scope for data integration and analysis.

Discover how to enhance sales growth effortlessly by utilizing automated data pipelines that connect seamlessly to data lakes or cloud storage solutions without the need for coding. This adaptable platform adheres to industry standards, enabling the integration of sales and marketing data to generate automated insights for more intelligent expansion. Eliminate the hassle and costs associated with cumbersome manual data downloads. You’ll always have a clear understanding of your expenses, only paying for the services you actually use. Empower your tools with rapid access to data that is ready for analytics. Our certified developers prioritize security by exclusively working with official APIs. You can quickly initiate data pipelines sourced from widely-used platforms. With pre-built, pre-transformed pipelines at your disposal, you can unlock crucial data from sources like Amazon Vendor Central, Amazon Seller Central, Instagram Stories, Facebook, Amazon Advertising, Google Ads, and more. The processes for data ingestion and transformation require no coding, allowing teams to swiftly and affordably harness the full potential of their data. Your information is consistently safeguarded and securely stored in a reliable, customer-controlled data destination such as Databricks or Amazon Redshift, ensuring peace of mind as you manage your data assets. This streamlined approach not only saves time but also enhances overall operational efficiency.

Speeding up the process of gaining insights and removing obstacles for data analysts is crucial. With the help of intelligent automation in the data stack, you can extract insights from your data much faster—up to ten times quicker—thanks to AI innovations. Originally developed at Stanford's AI lab, this cutting-edge intelligence for today’s data stack is now accessible for your organization. You can leverage natural language to derive value from your disorganized, intricate, and isolated data within just minutes. Simply instruct your data on what you want to achieve, and it will promptly produce the necessary code for execution. This automation is highly customizable, tailored to the unique complexities of your organization rather than relying on generic templates. It empowers individuals to securely automate data-heavy workflows on the modern data stack, alleviating the burden on data engineers from a never-ending queue of requests. Experience the ability to reach insights in mere minutes instead of waiting months, with solutions that are specifically crafted and optimized for your organization’s requirements. Moreover, it integrates seamlessly with various upstream and downstream tools such as Snowflake, Databricks, Redshift, and BigQuery, all while being built on dbt, ensuring a comprehensive approach to data management. This innovative solution not only enhances efficiency but also promotes a culture of data-driven decision-making across all levels of your enterprise.

Alkemi's premier offering, DataLab, serves as a secure AI-driven workspace that facilitates direct connections to your organization's regulated data from platforms such as Snowflake, BigQuery, Databricks, or even straightforward CSV uploads, allowing users to pose questions in everyday language and receive immediate, clear answers, visualizations, and suggestions without needing SQL expertise or analyst assistance. Within a private and secure environment, DataLab systematically indexes and examines your data, ensuring that every insight is both traceable and verifiable, maintaining the integrity of your data and protecting intellectual property and governance. By merging the complexities of data storage with accessible decision-making, it enhances the clarity of business intelligence through conversational AI, effectively minimizing BI backlogs and accelerating decision-making across various sectors, including marketing, finance, product, sales, and operations. Furthermore, DataLab empowers data providers to transform their datasets into engaging, AI-ready experiences that can be securely navigated by buyers, facilitating faster data discovery while safeguarding the integrity of the underlying raw data. This innovative approach not only streamlines workflows but also fosters a culture of data-driven decision-making within organizations.

TROCCO is an all-in-one modern data platform designed to help users seamlessly integrate, transform, orchestrate, and manage data through a unified interface. It boasts an extensive array of connectors that encompass advertising platforms such as Google Ads and Facebook Ads, cloud services like AWS Cost Explorer and Google Analytics 4, as well as various databases including MySQL and PostgreSQL, and data warehouses such as Amazon Redshift and Google BigQuery. One of its standout features is Managed ETL, which simplifies the data import process by allowing bulk ingestion of data sources and offers centralized management for ETL configurations, thereby removing the necessity for individual setup. Furthermore, TROCCO includes a data catalog that automatically collects metadata from data analysis infrastructure, creating a detailed catalog that enhances data accessibility and usage. Users have the ability to design workflows that enable them to organize a sequence of tasks, establishing an efficient order and combination to optimize data processing. This capability allows for increased productivity and ensures that users can better capitalize on their data resources.

CData Sync is a universal database pipeline that automates continuous replication between hundreds SaaS applications & cloud-based data sources. It also supports any major data warehouse or database, whether it's on-premise or cloud. Replicate data from hundreds cloud data sources to popular databases destinations such as SQL Server and Redshift, S3, Snowflake and BigQuery. It is simple to set up replication: log in, select the data tables you wish to replicate, then select a replication period. It's done. CData Sync extracts data iteratively. It has minimal impact on operational systems. CData Sync only queries and updates data that has been updated or added since the last update. CData Sync allows for maximum flexibility in partial and full replication scenarios. It ensures that critical data is safely stored in your database of choice. Get a 30-day trial of the Sync app for free or request more information at www.cdata.com/sync

The Ocient Hyperscale Data Warehouse revolutionizes data transformation and loading within seconds, allowing organizations to efficiently store and analyze larger datasets while executing queries on hyperscale data up to 50 times faster. In order to provide cutting-edge data analytics, Ocient has entirely rethought its data warehouse architecture, facilitating rapid and ongoing analysis of intricate, hyperscale datasets. By positioning storage close to compute resources to enhance performance on standard industry hardware, the Ocient Hyperscale Data Warehouse allows users to transform, stream, or load data directly, delivering results for previously unattainable queries in mere seconds. With its optimization for standard hardware, Ocient boasts query performance benchmarks that surpass competitors by as much as 50 times. This innovative data warehouse not only meets but exceeds the demands of next-generation analytics in critical areas where traditional solutions struggle, thereby empowering organizations to achieve greater insights from their data. Ultimately, the Ocient Hyperscale Data Warehouse stands out as a powerful tool in the evolving landscape of data analytics.

CelerData is an advanced SQL engine designed to enable high-performance analytics directly on data lakehouses, removing the necessity for conventional data warehouse ingestion processes. It achieves impressive query speeds in mere seconds, facilitates on-the-fly JOIN operations without incurring expensive denormalization, and streamlines system architecture by enabling users to execute intensive workloads on open format tables. Based on the open-source StarRocks engine, this platform surpasses older query engines like Trino, ClickHouse, and Apache Druid in terms of latency, concurrency, and cost efficiency. With its cloud-managed service operating within your own VPC, users maintain control over their infrastructure and data ownership while CelerData manages the upkeep and optimization tasks. This platform is poised to support real-time OLAP, business intelligence, and customer-facing analytics applications, and it has garnered the trust of major enterprise clients, such as Pinterest, Coinbase, and Fanatics, who have realized significant improvements in latency and cost savings. Beyond enhancing performance, CelerData’s capabilities allow businesses to harness their data more effectively, ensuring they remain competitive in a data-driven landscape.

Learn to understand your customers. Create a better product. PostHog offers a complete product analytics UX. Analyze trends, funnels and retention. Event autocapture is the key to all of this. PostHog automatically captures events and user behavior within your mobile or web app. Know how traffic flows through your app. Know the pageviews, actions, and other information of every user on your website or app. Visualize product trends and retention. Analytics can help you understand your users and how to keep them coming back. Visualize how users navigate your website or app. Use metrics to determine what needs improvement. Release new features regularly without worrying about breaking existing changes. Rapidly test new ideas and roll them out to 10%, 20%, or 100% of your users. PostHog can easily be deployed in your cloud for easy adoption and onboarding. PostHog is designed to scale. This includes our open core pricing model. PostHog can manage your deployment on your infrastructure.

Prodlytic offers a robust on-premise solution for behavioral analytics that automatically captures and preserves all event-level data from your website and web application. By continuously exporting this data to your preferred storage and analysis platforms, you can gain profound insights into customer interactions. With its auto-track feature, you can better understand your audience and enhance your product offerings without needing to manage complex tracking systems. Simply integrate our lightweight tracking code into your website or app once, and it will automatically monitor all customer interactions without the need for additional coding, even for new events. Prodlytic provides a comprehensive overview of user engagement metrics including clicks, page views, and form submissions, enabling you to make well-informed business choices. You have complete ownership of the data collected through Prodlytic, allowing you to seamlessly export it to familiar storage solutions like Amazon S3 and Google BigQuery for further analysis. This level of control empowers organizations to tailor their insights to their unique needs and strategic goals.

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.

Accelerate your data-driven decision-making by tenfold with self-service analytics that seamlessly integrates your databases and data warehouses in a single platform, including options like Postgres, MySQL, Snowflake, and BigQuery. Brewit simplifies the process by generating SQL queries and suggesting charts based on your specific data inquiries, while also allowing in-depth analysis. Engage in a conversation with your database, visualize findings, and conduct thorough analyses. With a built-in data catalog, you can guarantee the accuracy and reliability of your answers. An automated semantic layer ensures that Brewit applies the correct business logic in its responses. Managing your data catalog and data dictionary is straightforward, and creating an attractive report is as simple as crafting a document. A narrative brings your data to life, and our Notion-style notebook editor lets you effortlessly compile reports and dashboards, transforming raw data into valuable insights. Furthermore, all organized data products are accessible to anyone with a data-related query, making it easy for users of all technical backgrounds to engage with the information. In this way, Brewit democratizes data accessibility and empowers all users to utilize insights effectively.

Panoply makes it easy to store, sync and access all your business information in the cloud. With built-in integrations to all major CRMs and file systems, building a single source of truth for your data has never been easier. Panoply is quick to set up and requires no ongoing maintenance. It also offers award-winning support, and a plan to fit any need.

Capital One Slingshot is a powerful solution for cloud data platform management and optimization, designed to aid organizations in enhancing their utilization of Snowflake and Databricks. By offering improved visibility into financial and computational expenditures, it facilitates continuous monitoring, dynamic rightsizing, and AI-driven suggestions that aim to eliminate waste and inefficiencies while boosting overall performance. The platform features detailed dashboards and reports that track costs, usage, and performance trends, and it enables the allocation of expenses to specific business units through custom tagging. Additionally, proactive alerts inform users of credit usage and unexpected cost increases. Slingshot's recommendation engine thoroughly assesses workloads to optimize warehouse sizes, proposes adjustments to schedules, and identifies inefficient queries through its Query Advisor, ultimately enhancing SQL performance. Furthermore, it automates the optimization of Databricks jobs by leveraging machine learning models and supports comprehensive management and governance through customizable workflows and controls, making it a versatile tool for modern data operations. The integration of these features empowers organizations to achieve greater efficiency and cost-effectiveness in their data management strategies.

A comprehensive digital analytics and customer engagement solution designed for enterprises that empowers you to optimize every customer interaction and provide an enhanced experience through insightful data analysis. Gain a deep understanding of each customer touchpoint while taking immediate action to improve outcomes. Begin to elevate the customer experience from the very first day by automatically gathering behavioral data across all channels without the need for custom development. Each individual customer interaction is captured within a cohesive customer profile, enabling you to analyze their paths and journeys through intuitive visual dashboards. Delve into the reasons behind customer behaviors using ready-to-use analytics reports and Dataroid’s expansive data lake. Monitor engagement metrics in real-time to refine your communication strategies. Leverage Dataroid to gain deeper insights into customer behavior, improve the digital product experience, and boost customer retention effectively. By utilizing this platform, businesses can transform their approach to customer engagement and analytics.

Unravel Data is a powerful AI-native data observability and FinOps platform built for today’s complex enterprise data environments. It leverages intelligent Data Observability Agents to continuously monitor pipelines, workloads, and infrastructure for performance, reliability, and cost efficiency. Rather than just reporting issues, Unravel provides actionable insights that help teams resolve problems faster and prevent future incidents. The platform enables automated cost optimization, proactive troubleshooting, and performance tuning across the modern data stack. Unravel integrates seamlessly with existing tools and workflows, allowing teams to automate actions or maintain full control over decision-making. Purpose-built agents for FinOps, DataOps, and Data Engineering reduce firefighting, accelerate root cause analysis, and improve developer productivity. With native support for Databricks, Snowflake, and BigQuery, Unravel delivers deep, platform-specific visibility. Enterprises use Unravel to reduce cloud data costs, improve reliability, and scale operations confidently. Its agentic approach turns data observability into an active partner rather than a passive monitoring tool. Unravel empowers data teams to focus on innovation instead of constant issue resolution.

Vanna.AI is an innovative platform that utilizes artificial intelligence to facilitate user interaction with databases through natural language inquiries. This tool empowers users of all skill levels to swiftly extract valuable insights from extensive datasets without the need for intricate SQL commands. By simply posing a question, Vanna intelligently determines the appropriate tables and columns to fetch the required information. The platform seamlessly integrates with well-known databases such as Snowflake, BigQuery, and Postgres, and it is compatible with a variety of front-end applications, including Jupyter Notebooks, Slackbots, and web applications. With its open source framework, Vanna allows for secure, self-hosted installations and can enhance its functionality over time by learning from user engagement. This makes it an excellent choice for organizations aiming to democratize data access and streamline the querying process. Additionally, Vanna.AI is designed to adapt to the specific needs of businesses, ensuring that users can effectively leverage their data for informed decision-making.

Y42 is the first fully managed Modern DataOps Cloud for production-ready data pipelines on top of Google BigQuery and Snowflake.

Google Cloud's Analytics Hub serves as a data exchange platform that empowers organizations to share data assets securely and efficiently beyond their internal boundaries, tackling issues related to data integrity and associated costs. Leveraging the robust scalability and adaptability of BigQuery, it enables users to create a comprehensive library encompassing both internal and external datasets, including distinctive data like Google Trends. The platform simplifies the publication, discovery, and subscription processes for data exchanges, eliminating the need for data transfers and enhancing the ease of access to data and analytical resources. Additionally, Analytics Hub ensures privacy-safe and secure data sharing through stringent governance practices, incorporating advanced security features and encryption protocols from BigQuery, Cloud IAM, and VPC Security Controls. By utilizing Analytics Hub, organizations can maximize the return on their data investment through effective data exchange strategies, while also fostering collaboration across different departments. Ultimately, this innovative platform enhances data-driven decision-making by providing seamless access to a wider array of data assets.

SelectDB is an innovative data warehouse built on Apache Doris, designed for swift query analysis on extensive real-time datasets. Transitioning from Clickhouse to Apache Doris facilitates the separation of the data lake and promotes an upgrade to a more efficient lake warehouse structure. This high-speed OLAP system handles nearly a billion query requests daily, catering to various data service needs across multiple scenarios. To address issues such as storage redundancy, resource contention, and the complexities of data governance and querying, the original lake warehouse architecture was restructured with Apache Doris. By leveraging Doris's capabilities for materialized view rewriting and automated services, it achieves both high-performance data querying and adaptable data governance strategies. The system allows for real-time data writing within seconds and enables the synchronization of streaming data from databases. With a storage engine that supports immediate updates and enhancements, it also facilitates real-time pre-polymerization of data for improved processing efficiency. This integration marks a significant advancement in the management and utilization of large-scale real-time data.

When PostgreSQL serves as the foundation of your enterprise and is a key data source, PeerDB offers an efficient, straightforward, and economical solution for replicating data from PostgreSQL to data warehouses, queues, and storage systems. It is engineered to function seamlessly at any scale and is specifically adapted for various data repositories. By utilizing replication messages sourced from the PostgreSQL replication slot, PeerDB adeptly replays schema updates while providing alerts for slot growth and active connections. It also includes native support for PostgreSQL toast columns and large JSONB columns, making it particularly advantageous for IoT applications. The platform features an optimized query architecture aimed at minimizing warehouse expenditures, which is especially beneficial for users of Snowflake and BigQuery. Additionally, it accommodates partitioned tables through both publication mechanisms. PeerDB ensures rapid and reliable initial data loads via transaction snapshotting and CTID scanning techniques. With features such as high availability, in-place upgrades, autoscaling, advanced logging, comprehensive metrics, and monitoring dashboards, as well as burstable instance types, it is also well-suited for development environments. Overall, PeerDB stands out as a versatile tool that effectively meets the diverse needs of modern data management.

Mason continually improves with each query you submit. Effortlessly query, visualize, and share data with your colleagues. Utilize our advanced AI assistant to generate SQL and refine your results. Mason intuitively suggests and completes your joins, eliminating the hassle of sifting through a lengthy table list. Establish secure connections to BigQuery, Snowflake, PostgreSQL, and Redshift, providing all the tools necessary for effective teamwork. You can review different versions of your queries, track changes made by others, and revert to any previous state. Explore your team's queries in a shared library, and maintain focus by visualizing your data in customized dashboards tailored to your group. Receive direct feedback in your code, ensuring a streamlined workflow. Benefit from a data tool designed to maximize efficiency without wasting your valuable time. Navigate and execute actions from anywhere seamlessly, and remain in the zone with handy keyboard shortcuts. Experience the quickest method for querying, visualizing, and sharing data, with real-time synchronization offering interactions in under 100ms globally, ensuring your team stays connected and productive. The platform truly enhances collaboration by making data handling intuitive and fast.

Transfer your data seamlessly into your customer's cloud data lake or warehouse without ever having to leave your own system. By connecting Bobsled to your data source, you can select the specific bucket or warehouse for your data transfer, and Bobsled will take care of the rest. There’s no need to manage multiple accounts or construct complex pipelines. Designed on each platform’s sharing protocol, Bobsled offers data providers a secure and effortless way to share data, eliminating the challenges of managing a multi-cloud environment. Considering that data integration consumes 70% of the time that teams spend working with external datasets, Bobsled empowers your clients to quickly access analysis-ready data directly in the environments they are accustomed to. Additionally, users can easily track and manage every data share through a single interface, enabling them to initiate shares, automate data transfers, resolve any errors, and monitor usage efficiently. Ultimately, this streamlined process enhances productivity and allows teams to focus more on insightful analysis rather than tedious data logistics.

Espresso AI is a sophisticated data-warehouse optimization platform designed to lower compute and query expenses for services like Snowflake and Databricks SQL by utilizing machine-learning agents that handle scaling, scheduling, and query rewriting in real-time. It consists of three essential agents: an autoscaling agent that anticipates workload surges and cuts down on idle compute, a scheduling agent that efficiently directs queries across clusters to enhance utilization and minimize idle time, and a query agent that employs large language models along with formal verification techniques to rewrite SQL, ensuring that results remain consistent while enhancing performance. The system touts rapid deployment capabilities, claiming that users can get started in minutes instead of months, and features a pricing structure linked to the actual savings it generates, meaning you don't incur costs if it fails to lower your bill. By automating a vast number of optimization decisions each day, Espresso AI not only promises significant cost savings but also allows engineering teams to concentrate on developing features that add value. This innovative approach allows businesses to harness their data warehouse capabilities without the usual overhead, thus transforming the way they manage and utilize their data resources.