Data Engineering Drivers in Building Reliable Data Lakes
As we know that now a days businesses are going through unprecedented change and disruption and data is the most important business asset for these organisations which must be audited and protected because it plays a key role in helping organisations make better decisions to get the meaningful insights in their business. While data teams are generally growing, business demand for information and insights continues to surge much faster then Data engineering is the key to unlocking all other data-driven workflows.
To your business growth, you should always focus to increase your customers and always follow a good strategic choice for focusing during challenging times.
The world of data science is evolving so fast that it’s not easy to provide everything you need. To fulfill your need, data and analytics come together to help improve customer satisfaction by enhancing collaboration, facilitating communication. It has the effect of increasing productivity by releasing time for higher-value work to have a longer-term impact for the business.
Data Engineering Drivers — Data engineering professionals are needing to respond to several different drivers. Data engineering has evolved from focusing on just loading and storing with data lakes like Hadoop, to computing and extracting with Spark and Hive, to orchestrating with Oozie, Luigi and Airflow.
Rise of Advanced Analytics — This is the main driver for data engineering for a reliable data lakes which, including methods based on machine learning techniques, have evolved to such a degree that organizations seek to derive far more value from their corporate assets.
Through the use of AI and predictive analytics, any organizations can take big, seemingly random data sets and organize them to uncover hidden patterns and trends. This improves an organization’s access to information throughout an investigation, as traditionally this information is documented on a whiteboard.
Widespread Adoption — This is the another data engineering driver. As the digitalization of information has accelerated, yielding massive amounts of data, corporations have discovered that almost every dataset contains or can be explained using geographic insight and relationships. Once the province of leading edge, high-tech companies these advanced approaches are being adopted across a multitude of industries from retail to hospitality to healthcare and across private as well as public sector organizations. This is further driving the need for strong data engineering practices.
Rules and Regulation — Data is one of the most important assets a company has. With the rise of the data economy, companies find enormous value in collecting, sharing and using data. With the growth of data generation and data collection, there is increased interest in how the data is protected and managed.
The General Data Protection Regulation (GDPR) is the toughest privacy and security law in the world. Though it was drafted and passed by the European Union (EU), it imposes obligations onto organizations anywhere, so long as they target or collect data related to people in the EU.
Technology Innovation — This is the another data engineering driver. Technological innovation is the economic function through which new technologies are introduced into production and consumption. Move to cloud-based analytics architectures that is now well under way is being propelled further by innovations such as analytics-focused chipsets, pipeline automation and the unification of data and machine learning.
All these offer data professionals new approaches for their data initiatives. New innovations in data analytics and data science are powering more accurate decisions and outcomes. Optimizing and automating business processes allows for more meaningful insights to surface and enables the upskilling of people to accelerate a data driven culture across all levels of government.
Financial Scrutiny — with a growth in investment, analytics initiatives are also subject to increasing scrutiny. There is also a greater understanding of data as a valuable asset. Deriving value from data must be done in a manner that is financially responsible and actually value adding to the enterprise and meeting ROI hurdles.
Role Evolution — This is the another important data engineering driver for building a most reliable data lakes. It is reflecting the importance of managing the data and maximizing value extraction, the Chief Data Officer (CDO) role is becoming more prominent and newer roles such as Data Curator are emerging. They must balance the needs of governance, security and democratization.
References — Databricks,
