Data has become everything. It sees the past, evaluates the present, and predicts the future in almost every aspect of our modern-day businesses.
With the rise of big data, machine learning, and real-time data processing, data engineering has taken new strides in proving itself as a critical asset to help manage, analyze, and derive insights from data. With these new discoveries, businesses can make informed decisions, not on blind predictions but on data-based analysis.
Treating Data like a Product (Data Products)
Previously, data was primarily used to generate, consume, report, and analyze. Data engineering was focused on building pipelines to move data from one system to another or interconnect multiple systems.
With the rise of big data, few things about this framework have taken drastic changes. Data is now treated as a product and is not simply something that is produced and consumed but also something that is sold and marketed to customers.
To treat data like a product, data engineering teams have focused on building products that solve specific business problems. If it has a financial value and is a fix to a problem, then only data has the value of being a product. These data products are in dashboards, data APIs, or machine learning models that assist organizations in making data-driven decisions, improving customer experience, and skyrocketing revenue growth.
Key areas to focus on as a data product are ensuring data accuracy, reliability, and timeliness. As measures of keeping up with these requirements, data engineers must also implement data quality checks and validation processes to ensure your data product only provides high-quality data. Accurate, reliable, and real-time data are the foundation of good decision-making.
Power of SQL
Structured Query Language (SQL) has been around for over 40 years. It continues to play a vital part in modern data engineering. SQL is a standardized programming language to manage data stored in relational databases. This has incredible uses that allow data engineers to perform complex data transformations, aggregations, and filtering.
SQL plays an essential part in data visualization and reporting. With self-service business intelligence tools like Tableau and PBI, non-technical users can generate reports and dashboards without advanced coding skills.
SQL is also an essential tool for building data pipelines. Modern data engineering tools such as Apache Spark and Flink comprise SQL-like interfaces that make it convenient for data engineers to maneuver complex data tasks.
Bringing Machine Learning to Data Engineering
Machine learning has become a go-to component in modern data engineering. The ability to analyze incredible amounts of data and generate insights and data visualizations have assisted organizations in making data-driven decisions. As machine learning models are grown to equip automation processes, this has also improved operational efficiency.
Data engineers must have a good understanding of machine learning concepts and techniques to build effective data products tailored to your unique business needs. This comes through the ability to narrate machine learning models to specific requirements. Python programming alongside machine learning frameworks like TensorFlow and PyTorch are fundamental necessities for a successful machine learning data product.
Data preprocessing, featuring engineering and model evaluation, are also key data science concepts in data engineering. Training your machine learning data product with high-quality and problem-relevant data alongside data scientists is vital in building a successful machine learning data system.
Data in Motion (Streaming Data)
As organizations generate and gather these incredible amounts of data, data engineering teams must find a way to handle, coordinate and process these data. Data in motion refers to the concept of data continuously generated and processed as generated in real-time.
Apache Kafka is a commonly used open-source platform for real-time data streaming. As a distributed streaming platform that is purpose-built to handle millions of events per second, Kafka also provides scalability and fault tolerance as core values.
Kafka is widely used for real-time analytics, event-driven architectures, and microservices. It also comes with the ability to stream data from various systems, databases, IoT devices, social media platforms, and other data sources.
Integration is a crucial contributor to the Kafka platform for data engineering. The ability to work well with Apache Spark and Apache Flink provides excellent support in performing complex data transformations. Results can ideally be stored in databases or other data stores for further analysis.
Modern data engineering has evolved significantly in recent years, with the rise of big data, machine learning, and real-time data processing. Treating data like a product, the power of SQL, bringing ML to data engineering, and data in motion with Apache Kafka are all critical components of modern data engineering.
To be successful in modern data engineering, data engineers must have a good understanding of these concepts and technologies. They must be able to build data products that solve specific business problems, ensure that the data is accurate and reliable, and handle data in motion effectively.
As the volume of data continues to grow, data engineering will become even more critical to organizations. By adopting modern data engineering practices and leveraging the latest technologies, organizations can gain valuable insights from their data and make better decisions to drive growth and success.