Data management and processing have undergone a significant transformation over theyears. From the days of on-premise servers and manual orchestration to today’scloud-native solutions and automated pipelines, the evolution of data practicesreflects the ever-growing need for efficiency, scalability, and agility. Inthis blog post, we will explore how older approaches in infrastructure,deployment, code, and data management paved the way for the advanced systems werely on today.

Infrastructure

In the past, many databases ran on on-premise servers located directly in the office.However, hosting has now largely shifted to the cloud or dedicated data centers, making it rare to find local servers managed within offices by a dedicated system administrator. Similarly, advanced data processing was often performed on workstations situated on desks rather than on remote servers in data centers. While leveraging workstations can still be cost-efficient for stable workloads over extended periods, cloud-based solutions have become more appealing. This is due to the nature of data processing, which often involves fluctuating compute needs, and the increasing desire to process data faster, particularly given the high costs of Data Scientists and Engineers.

Deployment & Orchestration

Historically, deploying infrastructure code and applications relied heavily on tools like Chef and Puppet, and the process often required manual deployment without proper CI/CD pipelines. Modern practices, however, integrate DevOps principles, enabling full integration and deployment pipelines that automate the setup of infrastructure and applications. For orchestrating multiple servers, frameworks like Mesos and Yarn were commonly used to handle distributed application requirements. Today, Kubernetes has emerged as the dominant orchestration framework for both data and general applications. Tools such as Spark,SageMaker, and TensorFlow now natively support Kubernetes to meet their processing needs.

Observability

In the past, understanding errors often involved connecting directly to a server and manually processing logs through command line operations. This required strong command-line skills to retrieve and interpret log data effectively. However, observability tooling has significantly improved. Modern systems now collect logs using services like CloudWatch and surface them to tools like Elasticsearch, making it much easier to query and analyze errors without requiring deep command-line expertise.

Code & Techniques

Linear regression was frequently used in the past because of its computational efficiency. While compute costs have decreased significantly, enabling the use of more advanced modeling techniques to unlock the potential of larger datasets, linear regression remains useful but is less frequently chosen purely for computational reasons. Similarly, SAS was once favored for its ability to perform on-disk computation compared to alternatives like R or Pandas. AlthoughR and Pandas still struggle with on-disk computation, modern frameworks likeSpark and Polars have significantly improved in this area.

In the past, databases like Oracle supported running statistical models directly within SQL using procedural code to handle various processing needs. Running code close to data reduced latency and aligned with database languages like SQL. However, SQL’s limitations in expressiveness for tasks like visualization or creating graphical user interfaces often required additional programming languages. This approach gradually faded with the separation of processing and storage but is making a comeback with modern lakehouses like Snowflake. In these systems, Python and similar languages are now commonly used for processing needs.

Data Management

Data was often siloed in disconnected data marts, making interconnectivity a significant challenge. While data silos still exist, modern technologies like Postgres foreign data wrappers, data federation engines such as Presto/Trino, and modern lakehouses have greatly improved interconnection capabilities.

To accommodate many users, data was frequently replicated across multiple slave clusters for warehousing purposes. This often led to mismatched query results due to inconsistencies in replication. Cloud-based systems have mitigated these issues with built-in replication mechanisms, such as S3’s eventual consistency model and commit-per-file architecture. Distributed processing advancements, like sharding, have also reduced the need for manual interventions and improved reliability.

Traditional data warehouses required complex loading mechanisms with strict foreign key constraints. Records were often rejected if they did not meet these constraints, necessitating meticulous transaction management to ensure data integrity. Modern datalakes and lakehouses, which do not support multi-table transactions or foreign key constraints, require manually coded data flows to maintain consistency. Orchestration engines like Airflow handle these logical steps, but modern lakehouse frameworks typically only support single-table locks.

Conclusion

The evolution of data practices highlights the remarkable journey from rigid, manual, and localized approaches to flexible, automated, and distributed systems. While many older techniques and tools served as the foundation for today's advancements, they also reveal the challenges and limitations that once defined data management and processing. By embracing modern technologies like cloud computing, advanced orchestration frameworks, and improved observability tools, organizations have unlocked unprecedented scalability, efficiency, and innovation.

Looking back at these "old things in data" not only helps us appreciate the progress we've made but also provides valuable lessons for building the data systems of the future. As we continue to push boundaries, it’s clear that the future of data will be shaped by our ability to adapt and innovate, just as it always has been.

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