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How to Restructure Your Organization for the Data Engineering Revolution

The term “data engineering” has been around for years, but it is only recently that it has become a must-have discipline within organizations. The reason is simple: data has become the lifeblood of business, and the volume, velocity, and variety of data has exploded. The cost and complexity of managing this data has also increased exponentially. Organizations that want to stay ahead of the curve are turning to data engineering to help make sense of complex data and turn it into actionable insights.

However, data engineering is not easy because the tools and technologies, the required skills, the nature of data, and business needs are always changing. It is a complex discipline that requires a deep understanding of data, databases, and distributed systems and involves significant investments in hardware, software, and people. Data engineering also continuously requires care because data pipelines break, and data gets stale. The separation between the data engineering and data science teams in many organizations makes matters worse and often leads to conflict.

Because data engineering is still a relatively new field there is no one-size-fits-all solution. This lack of standardization means that each organization has to reinvent the wheel, which is both time-consuming and expensive. In order to build a successful data engineering strategy, organizations need to strategically blend talent and technology using some of the following principles:

1. Don't silo your data processing

One common mistake is to think of data processing as a series of isolated steps, each with its own dedicated system. For example, some organizations have a separate ETL process for each data source, a data warehouse for reporting, and a Hadoop cluster for analytics. Such a complex approach is difficult to maintain and limits the ability to take advantage of new opportunities. A better approach is to think of data processing as a continuous pipeline that ingests data from multiple sources, transforms it into a consistent format, and makes it available for downstream applications. The advantage of such an approach is that it is simpler and easier to maintain, amenable to new data sources and processing techniques, and easier to scale up.

2. Make use of data engineering platforms

There are a number of excellent open source data processing tools available, including Spark, Airbyte, and dbt that can help build a foundation for managing data pipelines to meet the ever-changing needs of data-driven organizations. Even better, there are integrated data engineering platforms, such as Spectre Data Platform, that can provide a comprehensive data stack along with integrated data operations that keeps your data current and ready to serve the needs of key stakeholders. These platform-as-a-service solutions meet the needs of data centric organizations, simplify data engineering tasks, and reduce headcount.

3. Don't forget about data quality

Data quality is often an afterthought in data processing pipelines, but it is an essential part of any data strategy. Poor data quality can lead to inaccurate results and flawed decisions. It is critical to elevate the importance of data quality in the overall data strategy, especially when it comes to acquiring and using third-party data.

4. Automate everything

Data processing pipelines are already complex, and their complexity is only going to increase as organizations rely increasingly on a combination of internal and external data to drive business success. The only way to manage this complexity is to automate data processing pipelines as much as possible. Automation saves time and effort and , helps ensure the reliability and consistency of data pipelines.

5. Build an efficient data engineering strategy

While there are a lot of tools and technologies available to help with data engineering, not all of them are created equal. Therefore, it is important to build an efficient data engineering strategy that includes choosing the right data engineering partner, selecting the right technology, and optimizing the size and constitution of the internal data engineering team. Organizations also need to strategically manage the triangulation among the internal data engineering team, the external data engineering partner, and the internal data science team. The overall goal should be to simplify the integration of data pipelines and compress the time from data identification to data analysis.

The role of the data engineering partner is critical in this triangulation because it can:

- Help automate and streamline tedious and time-consuming tasks

- Increase the productivity of the internal data engineering team

- Manage and monitor data pipelines

- Help troubleshoot problems

- Optimize data pipelines for superior performance

Even then, data engineering is hard and there is no silver bullet. However, it has quickly become a critical component of overall data strategy as organizations compete on the quality, speed, and integration of their external and internal data. It will soon emerge as a non-negotiable part of an organization’s overall competence and the foundation of its data-driven strategy.

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