How do you scale data workflows?

1 Data Architecture

Data architecture is the blueprint of how data is collected, stored, processed, and accessed in an organization. It defines the data sources, models, formats, standards, and policies that govern the data lifecycle. To ensure scalability, a data architecture should be able to accommodate new data sources, types, and volumes without compromising performance, security, or quality. Furthermore, it should be flexible enough to support different data consumption needs such as batch, streaming or real-time analysis. Some of the common patterns that enable scalability are: a Data Lake which stores raw and unstructured data from various sources in its native format; a Data Warehouse which stores processed and aggregated data for analytical purposes; and a Data Lakehouse which is a hybrid approach combining the benefits of both Data Lake and Data Warehouse. With this approach, features such as ACID transactions, versioning and time travel on the data can be leveraged through technologies such as Delta Lake, Apache Iceberg or Apache Hudi.

2 Data Pipeline

Data pipeline is the process of moving and transforming data from one or more sources to one or more destinations. It involves extracting, loading, transforming, validating, and delivering the data. To ensure scalability, a data pipeline should be able to handle large and diverse data sets, ensure data quality and integrity, and meet the service level agreements (SLAs) of the data consumers. It should also be resilient to failures, errors, and changes in the data sources or destinations. Data engineers must address common challenges such as data ingestion, transformation, and validation; for example, Apache Kafka, Apache NiFi, or AWS Kinesis can be used to create scalable and reliable data ingestion pipelines that can handle different data formats, rates, and schemas. Apache Spark, Apache Airflow, or AWS Glue can be used to create scalable and efficient data transformation pipelines that can perform batch, streaming, or hybrid processing on the data. Great Expectations, Deequ, or Soda can be utilized to create scalable and proactive data validation pipelines that can define, monitor, and alert on the data quality metrics and expectations.

3 Data Platform

Data platform is the infrastructure and environment that supports data workflows and enables data consumption and analysis. It includes hardware, software, network, security, and governance components that provide data storage, processing, and access capabilities. To ensure scalability, a data platform should be able to handle increasing data workloads and demands, as well as provide high availability, performance, and reliability. Additionally, it should be adaptable to changing business and technology needs while providing cost-effective and user-friendly solutions. Cloud computing, containerization, and serverless are some of the common aspects and technologies that enable scalability. Cloud computing leverages on-demand, elastic, pay-as-you-go models of cloud providers while offering services and tools for platform management. Containerization improves the consistency, efficiency, and portability of data applications by packaging them into isolated units called containers. Serverless abstracts away server management from developers by allowing them to focus on code logic of applications while reducing operational overhead. Scaling data workflows is a challenging task for data engineers requiring technical skills, architectural design, and operational best practices. By following best practices and tools available in the market, data engineers can create scalable workflows that deliver value and insights to their organizations.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?