Big Data Quality Validation Platform

1. Big Data Quality Validation Platform DataBuck is an autonomous, self-learning Big Data Quality validation tool. It is able to detect data quality errors without manual coding and provides an objective data trust score. The platform can significantly improve developer productivity by up to 10x compared to hand coding. The product's proprietary Data Fingerprint approach can reduce false alerts by as much as 85%. DataBuck is an autonomous, self-learning, Big Data Quality validation tool As a self-learning, autonomous Big Data Quality validation tool, DataBuck flags inaccurate records when they land in a data lake or move through a data pipeline. DataBuck leverages Machine Learning and AI to scan data assets and detect errors. It also monitors data health metrics to ensure data is of the highest quality. DataBuck can also enable self-service data quality checks, allowing Data Consumers to perform their own data quality checks. DataBuck works with AWS Glue, the leading data pipeline framework. For efficient data operations, validation of data in the pipeline is critical for accurate information delivery. It provides an automatic, secure and scalable solution for data validation. DataBuck is able to handle 1,000 data sets, including those from new sources. DataBuck uses AI/ML algorithms to discover business rules that automate a manual validation process. With just a few clicks, the tool can detect and fix 90% of system risks with minimal human intervention. It automatically sets more than 1,000 validation checks and thresholds, and can complete validation tasks 10x faster than other tools. Its unique machine learning algorithms can also validate entire schemas. Big data must be validated to ensure accuracy, completeness, and safety. As data moves through multiple IT platforms, it can be affected by unintended downstream processes. For example, improperly stored data may be corrupted. Additionally, poor data policies and processes can affect the integrity of data. DataBuck provides an automated, self-learning solution for Big Data quality validation. The solution automatically creates an independent data fingerprint for each dataset, allowing it to validate data against it automatically. Then, it automatically updates its fingerprints based on changes in the data. The process also reduces the need for manual rule maintenance. Business stakeholders can also view and control auto-discovered rules and thresholds, as well as have a complete audit trail. It detects data quality errors without coding In the era of untrustworthy data, the ability to detect errors in data assets is essential. If errors are left undetected, they can affect the entire asset. By leveraging AI/ML algorithms, DataBuck develops

2. standardized and customized metrics for each data set and publishes them to Alation's data catalog. Then, it monitors the metrics and transforms them into a data trust score. DataBuck's autonomous data quality validation software detects and eliminates 100% of systems risks with no coding required. It automatically sets thresholds and 1,000 validation checks, and can validate whole databases or schemas with minimal human intervention. It uses AI/ML algorithms to learn how to act in specific situations. It can also detect Data Quality deviations without coding, enabling non-technical users to validate a large volume of data without the need for IT resources. Data quality is becoming increasingly important, as data becomes a company's most important competitive asset, and it becomes a bottom-line issue. However, managing data quality is not easy, and many enterprises face serious problems as a result. DataBuck's free software solution can help enterprises get their data quality right. VISIT HERE DataBuck's architecture is linearly scalable, which means it doesn't get slowed down by massive data volumes. In contrast, conventional Data Validation tools rely on client server architecture, which is limited by their limited ability to process large volumes of data. The software also comes with an array of enterprise-class features and algorithms, so it doesn't need to be programmed. In addition to detecting data quality errors without coding, DataBuck can also improve data integrity and reliability. The company's award-winning software uses Machine Learning to detect and eliminate data quality errors. This software is able to detect up to 100% of a system's risks without any human intervention. Further, it's more than 10x faster than any other traditional approach. It creates an objective data trust score DataBuck is a machine learning-based data quality monitoring and validation tool. It monitors all data assets and creates an objective data trust score. It can automatically trigger data validation when new data is landed in a Snowflake table, or it can be scheduled to run periodically. Its web console interface allows you to provide a Snowflake connection and data to be analyzed. The results are presented in a graphical format that you can easily understand. DataBuck creates an objective data trust scoring system to eliminate the subjective opinion of data quality and integrity. By detecting problems before they affect downstream processes, the DataBuck solution helps to establish trust in data. By identifying the data-quality problems, it helps to eliminate the need for human review and verification. DataBuck uses machine learning algorithms to determine the uniqueness of individual records, thereby eliminating the need for data engineers to write data validation rules. By removing the need for human review, DataBuck can reduce the number of false alerts and false positives. A Fortune 500 industrial company, for example, reduced false alerts by 85% using DataBuck. This resulted in a $1.2 million saving.

3. Data quality is only part of the story. A reliable data environment must be accessible to line-of-business data users. A data that is difficult to use is useless if it is not easy to understand. Users must trust the data to make informed decisions. Talend's vision for trusted data includes data-management tools for line-of-business data users. It improves developer productivity 10x as compared to hand-coding DataBuck is a web development platform that claims to improve developer productivity 10x compared to hand-coding. The company claims that developers can complete benchmarks in a tenth of the time, while writing less code. This is based on data from studies that have been conducted by Tim Lister and Tom DeMarco. These games have been held since 1977 and pit teams of software implementors against each other to complete a set of benchmarks with the smallest possible number of defects and time. Using AI and machine learning algorithms, DataBuck automates the tedious validation process. Developers no longer need to manually validate thousands of fields and elements. The platform performs validations based on standardized rules and algorithms. These algorithms can validate entire schemas and databases without manual coding, and developer productivity is boosted 10x. In the past, developers have focused on time to "completion" when they are coding. However, it is important to understand that "completion" means that code is not ready for QA. Productivity is not about speed, but velocity. Productivity is also related to writing less bugs and more understandable, maintainable code. With DataBuck, developers can complete more code in less time and achieve their goals in less time than ever before. The productivity disparity is documented by multiple studies, but many managers still do not believe it. If they did, the factonists would win. This disparity is an important issue for companies, and data shows that DataBuck increases developer productivity by 10x compared to hand-coding. If managers believed this, it would be a victory for the factonistas.