Recently, we announced the industry’s first Data Engineering Cloud at the inaugural Wrangle Summit from Trifacta and Google Cloud. Being the pioneers in the industry with data preparation and data transformation for over a decade, we are in the right place to take it to the next level with data engineering. But why data engineering and why now?
Data is the new software
A recent article published by Andreessen Horowitz lists the five megatrends in the industry today. A key trend in this list is data, where it is mentioned that “data is the new software.” The efficiency of any application today is a direct reflection on the underlying data. What this means is that the data used by software applications and systems needs to be clean and useful for organizations to understand their business better for the required visibility into the road ahead. It is common knowledge that raw data is messy, unclean, complex, and is almost always unusable. The value of data engineering is to make this data usable. Data engineering is the process of integrating, preparing, and transforming raw data into useful data that can be used in analytics, data science, machine learning, cloud data warehouses, and more.
Data literacy and data quality
Individuals or teams working with data including data analysts, data engineers, and data scientists are always looking for effective ways of preparing and transforming data, generating efficient data models at any scale, and creating a self-service experience for themselves and their counterparts on the business side. However, they are often challenged with making sense of messy data that leads to inefficient systems and unanswered questions regarding business trends. Teams experiencing this pain refer to it as data illiteracy that can lead to unwanted business repercussions. The goal is to achieve data literacy providing individuals and organizations the ability to understand data along with the sources to gain insights.
Related to this is data quality and governance. Gartner estimates poor data quality costs organizations an average of $15 million per year. Addressing data quality proactively to ensure bad data does not enter the systems is a critical aspect of data engineering. Identifying outliers and preventing them from impacting both upstream and downstream platforms is key to effective data transformation. This is important as many organizations store redundant and stale copies of data at multiple places. Due to this inefficiency, downstream systems incorrectly interpret changes to upstream data sources with the wrong version of the data. To address this problem, it is imperative to build high quality data models, which will result in data standardization and responsible governance across teams. Eventually, this leads to the required insights into the business with accurate metrics.
The Trifacta Data Engineering Cloud
Accelerating data transformation, ensuring high quality data with governance, and addressing the requirements of anyone working with data is the foundation of the Trifacta Data Engineering Cloud. With a platform that is open, intelligent, self-service, and ready for the enterprise, the Trifacta Engineering Cloud provides the modern data workers with the tools needed to transform data, in any way they want to do it either with no code or low code, and at any scale to help them build innovative data products that can provide value to their business. So, let’s get started and foster innovation. Try Trifacta for free today.