Over the last decade or so, the importance of data and being data-driven has become apparent to all. Data-driven companies aspire to make business decisions based on actionable insights derived from business data in order to compete in today’s dynamic business environment.

 

To support and enable this trend, companies put together data engineering teams that are in charge of extracting (ingesting) data from organizational databases and SaaS services used by the business (Salesforce, advertising platforms, product usage data, etc), transforming the data into a format that could be consumed by data analysts down the road and loading the data into a data warehouse where it could be accessed by various stakeholders. Data engineers use ingestion tools to load data into the data warehouse and programming languages such as Python to code the transformations once the data is there. 

Companies then put together business intelligence teams, bringing in data analysts to analyze the data in the data warehouse to answer business questions and build reports and dashboards. Data analysts run SQL queries against the data warehouse to extract meaning and results from the data and use various visualization tools to produce reports.

 

Collaboration between the data engineering and business intelligence teams is key to enabling a successful data-driven strategy and business. Data engineers continuously load new data sources into the data warehouse, making changes to the data structure as they go along. Data analysts, bombarded by requests from the business to support an ever-growing list of reports and analyses, frequently have to go back to the data engineering team to request transformations and changes to the data structure. But data engineering teams are usually overloaded with work,  delaying the delivery of changes required by data analysts, causing frustration and negatively impacting business agility and decision-making.

 

The solution is to enable data analysts to transform data in the warehouse independently and judiciously, reducing their dependence on data engineering and reducing the load on the data engineering team overall. 

The problem is that data engineers use different technologies and practices from data analysts, making it hard for data analysts to take on this responsibility. It’s not only python vs. SQL – data engineers apply software development best practices while transforming data (code reviews, version control, automated testing, code documentation, CI/CD and more) to ensure clean, reliable and solid data transformation. These best practices are alien to most data analysts and require them to learn new skills, capabilities and approaches, instead of focusing on their critical role in analyzing data and providing the organization with actionable business insights.

 

Montara provides the bridge between data engineers and data analysts – a modern cloud-native BI solution that empowers data analysts to transform data in the data warehouse using SQL only, while enjoying all the benefits of software engineering best practices along the way: 

• Full version control that is completely transparent and managed by the platform 
• Simple and easy no-code automated testing as part of data pipeline execution
• Automatic documentation of every model  and auto-generated report-level lineage and data catalog to enable collaboration and data dissemination in the organization
• One-click deployment of model code changes to the production pipeline
• Simple and easy scheduling of pipeline execution with fully managed cloud infrastructure to run your transformation on your data warehouse.
• Ability for data engineers to trigger the pipeline from external processes to facilitate the collaboration.

 

With Montara data engineering teams can empower data analysts to share the load, transform data and run data pipelines in a controlled, managed environment. Data analysts can take on complex, important tasks of data transformation quickly and easily to keep them focused on their critical data analysis role.

Finally, we can work better with data, together.