Best Data Management Software for Secoda

Census is an operational analytics platform that syncs all your favorite apps with your data warehouse. Keep customer data in sync to bring together your customer success, sales and marketing teams. No engineering favors required. Census publishes SQL and dbt models automatically from your warehouse without you having to write a single line of code. Stop worrying about maintaining custom scripts or deciphering external APIs. Instead, focus on the business results. When everything is in your warehouse, you don't need "yet other source of truth". Census works on top your existing infrastructure. Pick a destination app and map the data. Voila! You shouldn't keep your data in quarterly reports. Every employee in your company can take action with Census. Live metrics in every app mean better business operations which results in happier users and more revenue.

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.

PostgreSQL, a powerful open-source object-relational database system, has over 30 years of experience in active development. It has earned a strong reputation for reliability and feature robustness.

Delta Lake is an open-source storage platform that allows ACID transactions to Apache Spark™, and other big data workloads. Data lakes often have multiple data pipelines that read and write data simultaneously. This makes it difficult for data engineers to ensure data integrity due to the absence of transactions. Your data lakes will benefit from ACID transactions with Delta Lake. It offers serializability, which is the highest level of isolation. Learn more at Diving into Delta Lake - Unpacking the Transaction log. Even metadata can be considered "big data" in big data. Delta Lake treats metadata the same as data and uses Spark's distributed processing power for all its metadata. Delta Lake is able to handle large tables with billions upon billions of files and partitions at a petabyte scale. Delta Lake allows developers to access snapshots of data, allowing them to revert to earlier versions for audits, rollbacks, or to reproduce experiments.

You can prevent data outages by identifying data quality issues and fixing them before they reach production. In less than a day, you can increase your test coverage for data pipelines from 0 to 100%. Automatic regression testing across billions upon billions of rows allows you to determine the impact of every code change. Automate change management, improve data literacy and compliance, and reduce incident response times. Don't be taken by surprise by data incidents. Automated anomaly detection allows you to be the first to know about them. Datafold's ML model, which can be easily adjusted by Datafold, adapts to seasonality or trend patterns in your data to create dynamic thresholds. You can save hours trying to understand data. The Data Catalog makes it easy to search for relevant data, fields, or explore distributions with an intuitive UI. Interactive full-text search, data profiling and consolidation of metadata all in one place.

Great Expectations is a standard for data quality that is shared and openly accessible. It assists data teams in eliminating pipeline debt through data testing, documentation and profiling. We recommend that you deploy within a virtual environment. You may want to read the Supporting section if you are not familiar with pip and virtual environments, notebooks or git. Many companies have high expectations and are doing amazing things these days. Take a look at some case studies of companies we have worked with to see how they use great expectations in their data stack. Great expectations cloud is a fully managed SaaS service. We are looking for private alpha members to join our great expectations cloud, a fully managed SaaS service. Alpha members have first access to new features, and can contribute to the roadmap.

Airflow is a community-created platform that allows programmatically to schedule, author, and monitor workflows. Airflow is modular in architecture and uses a message queue for managing a large number of workers. Airflow can scale to infinity. Airflow pipelines can be defined in Python to allow for dynamic pipeline generation. This allows you to write code that dynamically creates pipelines. You can easily define your own operators, and extend libraries to suit your environment. Airflow pipelines can be both explicit and lean. The Jinja templating engine is used to create parametrization in the core of Airflow pipelines. No more XML or command-line black-magic! You can use standard Python features to create your workflows. This includes date time formats for scheduling, loops to dynamically generate task tasks, and loops for scheduling. This allows you to be flexible when creating your workflows.