Every organization can benefit from data and use it in coordination with the organization’s goals. Today’s exponential growth in data velocity, volume, and variety is forcing enterprises to look for a platform that addresses their immediate needs and scales to meet their requirements. Modern organizations have data of multiple types and variable velocities, consumed by multiple personas on a continuous cycle. The organizations are also accountable, legally and ethically, to their customers for how they collect and store personal information.
Modern interactive analytics and machine learning demand continuous data processing as users experiment and iterate with different data types to arrive at the insights these tools can provide their businesses. Successful companies also implement financial governance controls that lead to cost savings when taking advantage of public clouds. This reduces guesswork and avoids difficult cost-controlling exercises.
Data Engineering
The term data engineer is relatively new, and the role appears only sporadically in technical literature. It is the data scientists who are exalted as modern-day heroes: the high-flying fighter pilots of big data who create machine-learning models for predictions and other analytics. Data engineering courses in India are the gateway to data science & analytics training & knowledge. During the 1990s, the world underwent a momentous transformation that reached more and more deeply into our lives and brought cascading impacts upon just about every career. The transformation was driven by a hodgepodge of trends:
- New roles for data throughout society, such as the growth of the web and social media.
- New sources of commercial data.
- The vast speed-up in telecommunications and the internet.
- New data sources such as sensors, cameras, and internet-enabled household devices.
- Inexpensive storage.
- Amazing new tools and algorithms for analytics, particularly machine learning.
As these changes took hold, the profession of data engineering emerged to gather, store, and distribute the data. The requirements and procedures in these professions are very different from the database administrator (DBA) job that attracted so many people starting in the 1980s. These major changes have taken place during the lifetimes of even fairly young employees.
Data engineering courses in India, as the name suggests, are a lot like software engineering. Data engineers learn to deal with such software engineering concepts as rapid and continuous development, automation and orchestration, modularity, and traceability. Operational concerns such as fault tolerance and service level agreements (SLAs) are handled differently in the age of data engineering. The appeal of clouds, both third-party (public) and on-premises (private), also alters the old equations while introducing new tools and processes.
The Role of the Data Engineer
The data engineer is the technical professional who understands how data analysts and data scientists need data and then builds the data pipeline(s) to deliver the right data, in the right format, to the right place. The best data engineers can anticipate the needs of the business, track the rise of new technologies, and maintain a complex and evolving data infrastructure.
Data engineers can face many challenges as organizations evolve their use of data beyond traditional reporting to data science, AI, and machine learning. The project backlog can be stressful and growing, putting pressure on the data engineering team. More data scientists and more data analysts mean more projects and demands for support from the data engineer. The changes to data are accelerating in small and large ways. It is called the “data drift”: The unexpected and undocumented changes to data structure, semantics, and infrastructure that are a result of modern data movement. Keeping up with data drift creates a huge burden on data engineers and platform operators, both to keep the lights on and ensure there are no disruptions to the analytics delivery. As data platforms evolve, for example, from on-premises data lakes and EDWs into public cloud services, data engineers are on task with huge re-platforming projects while still juggling their daily responsibilities.
In Summation
For data engineers, the public cloud provides numerous advantages to modernize toolkits with exciting new data services that scale way beyond the confines of traditional roles. However, simply shifting the legacy platform to the public cloud brings all the problems along with it. Data pipelines for the cloud need to address the elastic, scalable, and accessible nature of the cloud. Smart data pipelines take full advantage of these cloud attributes, while also detecting and being resilient to data drift. By developing the core capabilities through data engineering courses in India to land data into raw data lakes and data warehouses, enrich with real-time data from streaming services and event hubs, and transform data to be delivered to analytics teams and platforms, an individual can have the foundations for delivering fast, reliable insight to every corner of the business.
