Written by PriyaContent Writer
What is data engineering and its role within enterprise data strategy? Data engineers enable data-driven decision-making by designing, securing, operationalizing, and monitoring the data processing systems. They also require a practical understanding of operationalizing ML. In short, by creating complex data pipelines, data engineers are the intermediaries between data owners and data users. Furthermore, improving data quality ensures organizations can draw rich insights with ease.
Data engineers’ knowledge is referred to as DataOps because of the intersection of functional business understanding and software engineering methodologies that needs to be applied throughout the data lifecycle. With the help of cloud providers such as Google Cloud, data engineers no longer focus on managing infrastructure. The advantages include:
If interested in learning more about on-prem and cloud differences, especially when it comes to data warehousing, read our blog Data Warehouse Solutions—On-premise vs. Cloud
So, at what point do data engineering and meeting growth targets intersect? The first step is to model and create data schemas that help meet an organization’s objectives then build the systems to visualize, automate, and orchestrate the data. This allows data users and developers to use insights to increase revenue. With the help of managed services from public cloud providers, data engineers can develop comprehensive data solutions and have room to be open and innovative.
The E-Commerce industry shows how growth can be achieved through data engineering. The key growth areas are customer service through chatbots and calls, extending marketing reach via social media, SEO, etc. Several data entry points are needed to build the pipeline for data engineering. Setting up data events, identifying user data characteristics, search function data events, etc., are some of the must-haves for effectively developing a data process. The data can be captured via reviews, keywords, customer activity, product analysis, etc.
With the help of ingesting, transforming, and finally analysing internal and external data, organizations can detect trends, effectively segment the consumer base, and thus create better recommendations, improve searchability, and orient messages towards customer sentiment. As a result, businesses can offset CAC and increase retention with both data and customer-driven segmentation. And this is only possible by building data analysis systems that integrate ML workflows and templates.
At Royal Cyber, we have a team dedicated to becoming the extension of our clients and expanding their capabilities. Our data engineers engage in several best practices, including:
One of the critical tasks in data engineering is to ensure data visibility and enterprise data quality. Unfortunately, providing visibility and data governance across on-prem, the cloud, or multi-cloud is challenging. With cloud products such as Cloud Data Fusion, data engineers can have an easier time with the ETL process regardless of data type, structure, and source. In addition, DataFlow allows stream analytics, integrating stream events with TensorFlow and Vertex AI, and helps unlock further insights with an IoT platform. Thus, creating reusable data pipelines from their templates ensures that data quality is the focus of these two products alone.
Data warehousing is a significant component of the modern cloud data stack. It provides the ability to analyse historical data for building new data/ML models. With BigQuery, businesses can leverage benefits like separating compute and storage, allowing real-time data ingestion, and possessing inbuilt ML capabilities. In addition, based on Google Next ’21, this versatile data warehouse can be deployed across cloud platforms through BigQuery Omni, and thus can easily leverage the MongoDB database due to integration between the two platforms.
Google Cloud also eases managing the other components within the data pipeline. For example, Google Cloud provides Cloud Composer, a fully managed orchestration tool for Apache Airflow. In addition, recent integrations with Apache Sparkflow and Spanner PostgreSQL have expanded managed services within the cloud data stack. Thus, a wide selection of tools allows for consistent and efficient data processes.
As managed service partners of Google Cloud, we have Google-certified data engineers and analysts who can provide enterprise data quality solutions necessary to achieve growth. Additionally, with Google Cloud Products and Services, we have the means to create a modern, scalable data platform to help drive business growth objectives. Read this blog for an overview of the range of solutions Google Cloud offers.
When it comes to data engineering, we achieve this goal with the help of our data engineers, who can help extend and support client capabilities through: