As the industry moves towards self-serve models, it isn't easy to make informed choices about data tools. We know this can be overwhelming given the pace of change and the increasing complexity of these systems. Understanding the rapid rate of innovation is why we focus on open, standards-based architectures.
Tools, teams, and priorities change. Allowing customers the freedom to choose the data tools, business rules, models, or transformations drives productivity. As a result, our focus is consistently embracing current, and emerging standards for data access. Our approach to data accessibility and ease of use maximizes investments in your preferred tools and the people that use them.
Openbridge automates and unifies data for use in your favorite BI, modeling, processing, data visualization, SQL, or data science tools. Our standards-based, self-service data ingestion architecture ensures tools have quick access to analytics-ready data in Amazon Redshift, Amazon Redshift Spectrum, Google BigQuery, Azure Data Lake, Snowflake, or Amazon Athena.
One of our foundational tasks is caring for the risk associated with changes in upstream data from source systems. For example, what should happen when Amazon, Google or Facebook add or remove columns in their data feeds? Should the pipeline for that data "fail" gracefully, or should it adapt to the changes?
Continuous operation of data pipelines requires a data catalog to keep track of these changes, especially with a data lake. The Openbridge system is designed to adapt and adjust dynamically to changes it detects from various data sources resulting in data that is more understandable and easy to use.
Openbridge focuses on data automation, not providing data analytics reporting tools, visualizations, or BI. By focusing on open, standards-based data architecture, we ensure customers realize faster innovation and freedom from vendor lock-in. How? Standards-based, open data architecture provides customers free to choose from an incredibly diverse array of tools to explore, analyze, and visualize data to understand business performance.
For example, here is a small sample of industry-leading tools used by our customers;
Microsoft Power BI
Azure Data Factory
If you already have a preferred tool, like the ones listed above, great! Take a look at how to create custom models in your data destination to supercharge analytics and reporting:
However, if you are still exploring your options, there are great ones available to you. You have hundreds of options these days for business intelligence and data visualization tools.
Selecting Data Tools
The variety of analytic BI tools is continuously evolving, and it is not hard to quickly get lost trying to pick a tool. If you decide to compare all the software on the market and go to their websites, you will find that most of the content is generic and doesn't describe how they are different from each other.
All vendors promise ease of use, highly interactive dashboards, deep insights, and automation. We created a scorecard to help teams narrow down the choices:
Training, Training, Training
No matter how easy it is compared to others, any visualization tool will require training and patience. The better you understand how to use the tools, including data preparation, modeling, or visualizations, your teams will be effective. Be prepared for a longer journey than anticipated. However, while the journey may have taken longer, it will ensure the tools you have picked are right for you and your team.
Here are a few guides you may want to explore to help with the process: