It’s safe to say every company has data, but how we all handle it is different.  There are so many ways to store, use, and see cool insights from it that can help grow your business. We at Mosaic Software would like to introduce one of our Senior Product Engineers who has a lot of knowledge on how data pipelines enable the flow of data and the analytics to back it up. In this Q&A blog post, Ryan answers questions to help us understand data pipelines better.

Q: How Would You Pipeline Large Amounts of data?

A: Cloud providers such as AWS and Microsoft Azure provide powerful tools to manage large datasets. Azure Data Factory and AWS Data Pipeline are two great ETL tools that you could consider using if your data is hosted in one of those cloud providers. If you have data hosted on-prem then you can look at a number of other ETL tools such as SQL Server Integration Services. The common features between all these tools are pluggable connectors to a variety of different data sources and the ability to scale to the size requirements of your data. 

Q: What’s the best way to figure out what data you need and how you’ll combine, transform, and ingest it into your system?

A: A typical company has a lot of data already stored in databases somewhere and it’s difficult to know how best to use it. Don’t build data warehouses and data pipelines for the sake of building them. Focus on specific business use cases. Outline specifically the data that you need to accomplish the use case and only move the data that is required. For example, a company recently wanted to build a data warehouse to facilitate a more advanced analytics platform. Their source database had tables with over 300 columns. When building the data pipelines only the columns that were needed were brought over, which for one large table was a mere 12 columns out of 300. 

Q: How can data pipelines help make it easier to develop analytics and reports?

A: Data pipelines are a tool to transform your data from raw operational formats into a denormalized reporting format. When you build a data pipeline, your goal is not to simply move data around, but to alter the structure of the data to better meet the needs of your analytics or reporting applications. For example, if you have a database with position data for a fleet of vehicles, you can transform the data from a columnar format into a GeoJSON format that plugs directly into a frontend mapping tool. Data pipelines give you the flexibility to format data exactly the way you need it to bring value for the business.

Q: Should companies build their own data pipelines?

A: It depends on the use cases, complexity of the pipelines, the experience of the team, where the data is stored, and where it needs to go. Any company that needs to move data from one store to another is likely to find that building flexible, scalable pipelines are the best solution. 

Have other questions about data pipelines? Leave a question below or contact us here