Book Image

Driving Data Quality with Data Contracts

By : Andrew Jones
Book Image

Driving Data Quality with Data Contracts

By: Andrew Jones

Overview of this book

Despite the passage of time and the evolution of technology and architecture, the challenges we face in building data platforms persist. Our data often remains unreliable, lacks trust, and fails to deliver the promised value. With Driving Data Quality with Data Contracts, you’ll discover the potential of data contracts to transform how you build your data platforms, finally overcoming these enduring problems. You’ll learn how establishing contracts as the interface allows you to explicitly assign responsibility and accountability of the data to those who know it best—the data generators—and give them the autonomy to generate and manage data as required. The book will show you how data contracts ensure that consumers get quality data with clearly defined expectations, enabling them to build on that data with confidence to deliver valuable analytics, performant ML models, and trusted data-driven products. By the end of this book, you’ll have gained a comprehensive understanding of how data contracts can revolutionize your organization’s data culture and provide a competitive advantage by unlocking the real value within your data.
Table of Contents (16 chapters)
Part 1: Why Data Contracts?
Part 2: Driving Data Culture Change with Data Contracts
Part 3: Designing and Implementing a Data Architecture Based on Data Contracts

The enterprise data warehouse

We’ll start by looking at the data architecture that was prevalent in the late 1990s and early 2000s, which was centered around an enterprise data warehouse (EDW). As we discuss the architecture and its limitations, you’ll start to notice how many of those limitations continue to affect us today, despite over 20 years of advancement in tools and capabilities.

EDW is the collective term for a reporting and analytics solution. You’d typically engage with one or two big vendors who would provide these capabilities for you. It was expensive and only larger companies that could justify the investment.

The architecture was built around a large database in the center. This was likely an Oracle or MS SQL Server database, hosted on-premises (this was before the advent of cloud services). The extract, transform, and load (ETL) process was performed on data from source systems, or more accurately, the underlying database of those systems. That data could then be used to drive reporting and analytics.

The following diagram shows the EDW architecture:

Figure 1.1 – The EDW architecture

Figure 1.1 – The EDW architecture

Because this ETL ran against the database of the source system, reliability was a problem. It created a load on the database that could negatively impact the performance of the upstream service. That, and the limitations of the technology we were using at the time, meant we could do few transforms on the data.

We also had to update the ETL process as the database schema and the data evolved over time, relying on the data generators to let us know when that happened. Otherwise, the pipeline would fail.

Those who owned databases were somewhat aware of the ETL work and the business value it drove. There were few barriers between the data generators and consumers and good communication.

However, the major limitation of this architecture was the database used for the data warehouse. It was very expensive and, as it was deployed on-premises, was of a fixed size and hard to scale. That created a limit on how much data could be stored there and made available for analytics.

It became the responsibility of the ETL developers to decide what data should be available, depending on the business needs, and to build and maintain that ETL process by getting access to the source systems and their underlying databases.

And so, this is where the bottleneck was. The ETL developers had to control what data went in, and they were the only ones who could make data available in the warehouse. Data would only be made available if it met a strong business need, and that typically meant the only data in the warehouse was data that drove the company KPIs. If you wanted some data to do some analysis and it wasn’t already in there, you had to put a ticket in their backlog and hope for the best. If it did ever get prioritized, it was probably too late for what you wanted it for.


Let’s illustrate how different roles worked together with this architecture with an example.

Our data generator, Vivianne, is a software engineer working on a service that writes its data to a database. She’s aware that some of the data from that database is extracted by a data analyst, Bukayo, and that is used to drive top-level business KPIs.

Bukayo can’t do much transformation on the data, due to the limitations of the technology and the cost of infrastructure, so the reporting he produces is largely on the raw data.

There are no defined expectations between Vivianne and Bukayo, and Bukayo relies on Vivianne telling him in advance whether there are any changes to the data or the schema.

The extraction is not reliable. The ETL process could affect the performance of the database, and so can be switched off when there is an incident. Schema and data changes are not always known in advance. The downstream database also has limited performance and cannot be easily scaled to deal with an increase in the data or usage.

Both Vivianne and Bukayo lack autonomy. Vivianne can’t change her database schema without getting approval from Bukayo. Bukayo can only get a subset of data, with little say over the format. Furthermore, any potential users downstream of Bukayo can only access the data he has extracted, severely limiting the accessibility of the organization’s data.

This won’t be the last time we see a bottleneck that prevents access to, and the use of, quality data. Let’s look now at the next generation of data architecture and the introduction of big data, which was made possible by the release of Apache Hadoop in 2006.