Zero administration, automated data catalog
Ever have columns added to or removed in data from multiple sources? Do data types ever change in source systems? The Openbirdge data catalog captures and manages upstream data changes, including automatically versioning tables and views in your data lake or cloud warehouse.
Data is analyzed, and the system trained when creating your data catalog. Resulting data governance rules trigger the automated creation of databases, views, and tables in a destination warehouse or data lake.
Delivering data integrity and accuracy
Machine learning transformations are used to de-duplicate data assets from real-time or batch source systems. We use machine learning algorithms behind the scenes to learn how to identify duplicate records prior to loading data into a target system.
Our model training uses a constant stream of source data to fuel machine learning algorithms. Once trained, de-duplication transforms run as part of a regular data pipeline workflow, no machine learning expertise required.
Hassle-free, automated job scheduling
Our job scheduler automatically evaluates when, where, and how to run jobs for each data pipeline. All pre-built pipelines run in exactly the order in which a source system will supply accurate, complete data. Each workflow automates all dependencies to meet source system APIs requirements including data availability, capacity planning for large volumes of data, rate limits, error handling, and versioning.
Automatic data partitioning for transformed data in your lake
Our optimization approach improves performance by reducing the cost of data stored in your lake. Data partitioning minimizes errors from queries that run across objects while increasing the performance by limiting the data in the scope of a request.
"Very early into our journey, we knew how essential data was in driving innovation and growth. Thanks to Openbridge’s data lake solutions and technologies, our marketing, operations, and sales data is ready-to-go for insights and analysis efforts.
Automatic conversion of your data sets to Apache Parquet
We convert data into an efficient and optimized open-source columnar format, Apache Parquet. Using Parquet lowers costs when you execute queries as the files columnar format optimizes for data lakes and interactive query services like Azure Data Lake, AWS Athena, or Redshift Spectrum.
Parquet is up to 2x faster and consumes up to 6x less storage in Amazon S3, compared to text formats like CSV.
Parquet files are highly portable; they support being used as the data objects for external tables in other destinations like Snowflake, Google BigQuery, or Databricks.
Enhancing data literacy with automated metadata generation
When we deliver data to a destination like Azure Data Lake, BigQuery, AWS Athena, AWS Redshift, or Redshift Spectrum, we append additional metadata unique to the information resident in a record. Your tables and views will include a series of system generated fields that provide users with vital information about the meaning of the data we collected on your behalf.
This provides a critical context about a record, but it simplifies queries and data modeling.
Saving time and money with Google Snappy compression
Data compression is performed column by column using blazing-fast Google Snappy.
Google developed the Snappy compression library, and, like many technologies from Google, it was designed to be efficient and fast. By employing Snappy, we enable teams to realize query optimizations by reducing the size of the data stored in your data lake. Our compression approach equates to higher performance and reduced operational costs.
On-the-fly routing of batch or raw data to target systems like Amazon Redshift, Google BigQuery, or Amazon Athena
Data routing allows you to easily map a data source to a target destination. This allows you to easily partition according to preferred data lake, data warehousing, and data governance strategies.
CSV file testing + schema generation
Comma-separated values (CSV) is commonly used for exchanging data between systems. Our free public API and client software allow data analysts, engineers, or data scientists the ability to determine the quality of CSV data before delivery to data pipelines.
Our API service will validate a CSV file for compliance with established norms such as RFC4180. The API will generate a schema for the tested file, which can further aid in validation workflows. Not ready to use the API? You can use our quick and easy browser application to test your files.
Standards based, open access by design
Applying industry standards and best practices for extract, load, transform (ELT) or extract, transform, and load (ETL) ensure our data engineering and architecture delivers consistent and easy access to your data. Regardless of the data tools your team of data scientists, analysts, IT, or business execs want to use, open and flexible standards are critical.
Our "analytics-ready" model maximizes investments in your people and the tools they love to use. By consistently embracing current and emerging standards-based data access, we deliver maximum flexibility and compatibility.
Don't go it alone solving the toughest data strategy, engineering, and infrastructure challenges
Building data platforms and data infrastructure is hard work. Whether you are a team of one or a group of 100, the last thing you need is to fly blind, and get stuck with self-service (aka, no service) solutions.
You have a project. We have expertise. Let’s put it to work for you!
Free your team from painful data wrangling and silos. Automation unlocks the hidden potential for machine learning, business intelligence, and data modeling.
80% of an analysts’ time is wasted wrangling data. Our platform accelerates productivity with your favorite data tools to save you time and money