Superconductive, the creators of Great Expectations, announced recently that it has raised $40M in its series B funding for the commercial version of its open source data quality tool. This is great news to many data engineers as Great Expectations is one of the most popular open source data quality tools organisations use today.
This article will dive into what makes Great Expectations such a great open source data quality tool and how organisations can benefit from it.
Overview of Superconductive
Superconductive, creators of Great Expectations, strive to provide an open source tool for Data Quality assurance that is unlike anything else on the market. Developed by the team of experienced data engineers and architects, this simple yet powerful tool allows you to quickly validate data accuracy and completeness while offering reusability across projects and teams.
Using Great Expectations, you can automatically track the performance of data with project-level KPIs snapshots covering hundreds or even thousands of elements in near real-time.
With its open source nature, Superconductive designed Great Expectations as an inclusive platform for Data Quality assurance for all users. With detailed online documentation and extensive forum support from industry veterans ready to help troubleshoot any technical issues you may encounter along your journey, Superconductive has laid down a solid foundation to advance your analytics projects faster than ever before.
What are Great Expectations?
Great Expectations is a free, open-source data quality tool designed to help teams operationalize data governance and quality assurance. Great Expectations helps users proactively manage trust and reliability in the data used to make critical decisions, detect errors before they cost time, money, and reputation, and increase speed to insights by automating tedious data checks.
Developed and maintained by members of the open source community, the Great Expectations package includes a powerful set of tools for profiling raw datasets; pre-configured validation and expectation suites; easy-to-configure visualisations; dashboard widgets; compliance reporting; user notifications and notifications via email, communication channels like Slack, or webhooks; utility functions that can run as part of automated processes (e.g., on dbt models); custom parameterized checks for domain-specific validations (using Python or Spark code); support to validate large datasets using Apache Spark; integration with other popular open source tools like pandas, dbt, Airflow etc.; comparison tools for testing results from databases such as Hive/Impala/BigQuery against each other or external sources; alerting framework with configurable thresholds for confidence intervals used in reporting.
Features of Great Expectations
Superconductive, creators of Great Expectations, recently announced $40M in Series B funding for the commercial version of its open source data quality tool. It enables developers and data teams to test and validate data pipelines.
This section will discuss some of the features of this open source data quality tool and how it can enable developers and data teams to ensure data accuracy and quality.
Data Quality Assurance
Great Expectations is a platform that assists businesses in meeting the ever-increasing demand of data quality assurance. Its features ensure efficient and reliable data processing to help organisations succeed in their data-driven decision making process.
Great Expectations focuses on three key principles when it comes to data: validity, accuracy, and completeness. It offers a comprehensive set of tools for verifying that large datasets contain only valid, accurate and reliable records and that no valuable information is lost along the way. In addition, Great Expectations ensures confidence in data by providing easy access to statistical summaries, strategic diagnostics and proactive monitoring capabilities, enabling organisations to quickly identify underlying issues with their data pipelines.
In addition, Great Expectations features built-in tests for verifying accuracy such as calculating basic statistical values like mean, mode and median; checking for missing values; detecting outliers; validating column types (numeric/string/date) based on user specifications; cross-checking referential integrity by looking up columns in dictionary types etc. It also provides an alert system which notifies users when dataset thresholds or characteristics fall out of predetermined ranges or change spontaneously. This allows users to spot problems caused by incorrect assumptions or outdated patterns – these which would otherwise be hard to detect manually over large datasets.
Finally, Great Expectations supports various formats such as CSV, JSON and Parquet making it simple for all types of businesses – from small startups to large enterprises – adhere to widely accepted data quality standards without the need for costly custom implementations or vendor lock-in contracts!
Automated Data Validation
Automated data validation is an important part of the “Great Expectations” framework, which helps companies develop and manage data quality expectations and build trust in their data. Data validation is verifying the accuracy, completeness, consistency and reliability of the data being processed by software. Automated solutions quickly and efficiently check incoming data against rules based on a company’s specific requirements.
The automated processes within Great Expectations leverage machine learning techniques for efficient model training and incorporate pre-defined statistical methods to ensure consistency between datasets. This helps teams develop trust in their data without sacrificing valuable resources or adhering to laborious manual processes.
The automated validations using Great Expectations also enable audits of a company’s entire pipeline for ongoing updates on customer requirements or other changes in specifications about the format and contents of their datasets. This ensures that an organisation consistently leverages quality insights from clean, reliable customer data that meets all customer expectations.
Data documentation is an important aspect of any project involving data analysis and mining. It describes creating, maintaining, and updating the data sets and elements used in a specific research. In addition, data documentation aims to ensure the accuracy and reliability of datasets used in a project so that results can be consistent throughout the entire process. This includes providing pertinent information on how to interpret the variables contained in a dataset and what processes have been used to generate them.
Data documentation should provide a clear summary of the data sources incorporated into a project, such as type (numeric or categorical) or range (if applicable). Additionally, variable definitions must be thoroughly explained to properly interpret results later. All data manipulation processes including transformation, reshaping and extracting should also be documented according to established standards to allow proper replication of study results. Further details such as notes specifying outliers or statistical assumptions might also need to be included when relevant so that each dataset is carefully annotated throughout its use. Ultimately, good documentation of data sets helps protect against discrepancies and make work more efficient for other researchers.
Superconductive, creators of Great Expectations, nabs $40M for a commercial version of its open source data quality tool
Superconductive, creators of the open source data quality tool Great Expectations, recently raised $40M in funding to expand its presence.
Great Expectations helps organisations analyse and clean up data quickly, ensuring accuracy and reliability. It can also help organisations create transparency and trust in their data quality by providing a detailed data quality report.
This article will look at the benefits of using Great Expectations.
Improved Data Quality
A key benefit of using Great Expectations is improved data quality. This enables businesses to make better decisions, as they can trust the reliability and clarity of data. In addition, organisations can leverage their existing analytics by having uniform data that’s clean and up-to-date.
Great expectations also improve the accuracy of results when used with machine learning algorithms, or any process that relies on prediction models. As many machine learning processes are based on estimation, correct dataset inputs are essential to ensure successful outcomes with reasonable confidence scores. In addition, clean data prevents costly mistakes due to erroneous conclusions from inaccurate information.
Furthermore, Great Expectations can be used for automated detection of suspicious activity in databases, ensuring an effective way to manage potential flaws or inaccuracies in datasets. By checking for correctness and completeness at an individual record level, businesses can protect their valuable resources rather than relying on end users to manually check for errors, which often introduces greater mistakes.
All these benefits manifest themselves in improved decision making capability and a reliable basis for continued improvement in machine learning accuracy. These are key businesses seeking new ways to leverage data more efficiently and achieve greater insights from their current offerings.
Great Expectations helps to organise and centralise processes, making a company’s workflow more efficient. The software automates many tasks to save time and money, allowing staff to focus on more important elements of their job. With Great Expectations, users can manage data faster and more easily since all pertinent information is stored in one place. This eliminates the need for employees to search multiple databases for the same information.
Additionally, accuracy improves with automated processes. Erroneous manual data entry is eliminated by utilising integrated workflows within Great Expectations and identifying flaws and errors in existing processes before they become a significant issue. This reduces costs associated with mistakes while upholding a consistent standard of quality. Furthermore, real-time reporting systems allow stakeholders access to timely insights on their organisation’s performance at any given moment, highlighting irregularities or opportunities quicker than any other manual means of identification would allow. Companies will gain an edge over competitors by recognizing potential issues ahead of time using greater visibility into their processes.
Great Expectations encourages best practices in data engineering and helps reduce risk in corporate data pipelines by increasing data quality visibility. Using Great Expectations, users can set data quality standards that can be applied across their entire team’s datasets, making it easier to ensure that all projects comply with internal governance rules. Additionally, it allows businesses to better gauge the effectiveness of their current methods for managing data pipelines and make adjustments based on the results — essential for any organisation seeking to remain competitive.
In addition to its ability to streamline processes, Great Expectations also provides business with several direct benefits:
- It eliminates manual checks from the pipeline process and removes a potential source of human error.
- Automatic warnings are displayed for flagged values or datasets so compliance issues can be addressed quickly.
- Easily customizable tests allow organisations to set thresholds and standards specific to their needs.
- Exceptions are aggregated so that users can see all potential issues in an easily understandable format.
- Users have access to detailed reports on data quality metrics which allows them to track trends over time and identify areas where improvement might be needed.
Superconductive’s Funding Announcement
Superconductive, the creators of the open source data quality tool Great Expectations, have recently announced a $40M Series B funding round to help bring a commercial version of their product to market.
This funding announcement highlights the growing demand for open source data quality solutions. Furthermore, it is a sign of the commitment from Superconductive to helping users manage and operationalize their data.
Overview of the $40M Funding Round
Superconductive has successfully raised $40 million through a Series A financing round to accelerate the development of its identity-based customer engagement platform. The fundraising round was led by Goldman Sachs Investment Partners, and included participation from new investor Tiger Global Management and existing investors Formation 8 and True Ventures.
Superconductive’s identity-based customer engagement platform connects customers with brands in real-time, providing personalised mobile and web experiences. By focusing on data from users’ profiles, Superconductive provides contextualised content that supports customers throughout their journeys and delivers tangible business value.
The capital will be used for product development, sales expansion, marketing activities, and boosting its engineering team. In addition to accelerating product development and advertiser growth across Web & Mobile experiences with real-time personalization at scale — Superconductive will also invest in expanding into Asia Pacific markets where the demands for rich user experience design is beginning to take shape.
In addition to its cutting-edge technology solutions, Superconductive has built its leadership team with proven leaders from top brands like HP, Microsoft & Apple: Scott Hornstein (CEO), Aaron Hurless (CFO) & Shriram Sarin (CTO). Together they are setting the course for transformational technology solutions that focus on connected identities across web & mobile channels through real-time personalization at scale.
With this new funding round, Superconductive is looking forward to building out an ecosystem of partners around it’s technology suite thus putting it’s solutions on alternative channels of commercial delivery as well further empowering transparency between companies & customers as a means of enhanced customer engagement along every step of the consumer journey towards becoming a power user within machine learning technologies dominated sphere..
Potential Impact of the Funding
The funding secured by Superconductive will have a tremendous impact in the semiconductor industry. It expands the company’s capabilities beyond research and innovation and opens up a new horizon of possibilities. Superconductive plans to invest heavily in its materials division with the additional capital, providing faster, more efficient solutions to customers.
Superconductive will also utilise the funds to expand its unique Intelligent Network Technology (INT) platform for efficient machine-learning and data science tasks. With INT integration on select customer devices, consumers can benefit from real-time analytics and insights into their connected semiconductor systems. Further breakthroughs are expected from outcomes that leverage increased computing capacity from INT use cases.
More importantly, with additional investments in product development engineering, Superconductive seeks to revolutionise how products are designed from the lab bench to commercialization. The company is investing in innovative design tools for automating a vast array of semiconductor product development tasks such as prototyping, testing, debugging and optimization. This means faster timelines for production quality products that have passed stringent industry standards – which ultimately helps make end user experiences even better.
Finally, with its technological edge firmly established, Superconductive can now focus on emerging markets when evaluating new opportunities or expanding its existing operations into new territories or sectors – opening up potential channels of growth that were difficult to access before this funding announcement was made public.