Data Engineering Best Practices

Data Engineers Share Best Practices as Experts on Demand

In today’s data-driven environment, data engineers can tell you that making informed decisions is paramount for optimizing business processes. However, dealing with a vast amount of data can be daunting. This is where data engineering, a systematic approach to transforming raw data into a comprehensible and valuable resource for business growth comes into practice. Data engineers help in creating and maintaining the infrastructure/system necessary for efficient data processing and have many standard best practices. Let us explore them and simultaneously discuss why hiring data engineers as experts on demand will be beneficial across the board.

Data Engineering and Data Engineers

Data Engineering refers to a methodical process of converting raw data into a format understandable for people who do not have prior experience or knowledge in dealing with the original form of data. The process makes it easy for stakeholders to understand or grasp the current situation of their business and how they are performing in numerous verticals.

Let us break it down further…

Imagine companies as big puzzle-makers. They have lots of puzzle pieces (data) scattered around. These puzzle pieces come from different sources, like sales, pre-provided customer info, website traffic, etc.

Companies want to solve important questions, like “How well are we doing?” or “What should we improve?” To answer these questions, they need to put the puzzle pieces together.

Here’s the catch: The pieces are in different shapes and sizes.

Data engineering helps to solve the puzzle, organizing and fitting the pieces together, in a format that everyone can understand. So, in addition to data engineers and data analysts, managers and stakeholders can also comprehend the complete picture.

With the help of tools or software, data engineers bring all the accumulated data under one single view, filter out what’s not needed, and process the data by organizing and packaging it in forms that are easy to read and understand. A true wizard-like profession!

In this data-rich world, data engineers empower key decision-makers by providing them with accessible data that is both important and relevant for future business decisions.

What are data engineering best practices?

Data engineers have a high-stakes job as many business decisions rely on the data they provide. A single mistake can impact a company’s finances and customer base. Therefore, it is essential to establish a list of mandatory best practices to ensure that the entire process is carried out efficiently. Furthermore, these practices will help reduce errors and streamline workflows.

Here is a list of four data engineering best practices:

  • Scalability: Maintaining scalability is crucial for data engineering roles. For data engineers, scalability refers to the system’s capacity to handle growing data volumes and user demands without compromising performance. This is essential because as data accumulates over time, the system must gracefully accommodate more data without faltering. In recent years, data engineers have turned to technologies like Apache Kafka, Apache Hadoop, and various cloud-based services to adopt innovative scaling methods.
  • Quality Assurance: Ensuring data quality is another important requisite in the field of data engineering. Data plays a vital role in the successful functioning of companies, regardless of their size and shape. Poor data quality can result in flawed decision-making, leading to revenue losses and project setbacks. To uphold data quality according to company standards, data engineers should focus on these key factors:
  1. Accuracy: Data engineers diligently verify that the data is correct and free from errors. This attention to accuracy is particularly critical when handling sensitive data, such as medical records, sales figures, and tax records.
  2. Consistency: Consistency refers to the degree to which data adheres to a uniform format across various systems and applications. Inconsistencies in data can cause confusion, errors, and operational inefficiencies.
  3. Completeness: Maintaining data completeness is essential. It ensures that data contains all the necessary information. Incomplete data can lead to unfavorable decisions, resulting in financial losses and other adverse impacts.
  4. Timeliness: Timeliness in data quality means having up-to-date information available when it is needed. Outdated or delayed data can cause missed opportunities, wrong decisions, and lost revenue. To ensure data timeliness, data engineers use real-time data tools, automate processing, and optimize storage and retrieval systems.
  • Performance Optimization: Performance optimization makes sure that data is available at the hour of need and is coming from reliable sources. Efficient data processing yields timely insights and real-time results, aiding sound business decisions. To maintain efficient data flow, data engineers focus on the following factors:
  1. Ensuring a robust, error-free system: A strong system minimizes errors and ensures smooth data processing.
  2. Controlled login access and robust monitoring: Secure login access prevents unauthorized data access. Additionally, robust monitoring tracks system performance and identifies bottlenecks.
  3. High-quality data: Data engineers validate and clean data to maintain its quality. High-quality data leads to accurate analyses and informed decisions.
  4. Documentation of important changes: Documenting changes ensures transparency and facilitates collaboration.
  • Data Protection and Compliance: Data Protection and Compliance: Data engineers deal with confidential data daily. A lot is at stake when there is a case of data breach or data leak. Therefore, ensuring data is handled and shared responsibly and discreetly is a must-do for every data engineer. Companies, now, religiously perform essential security measures such as the installation of firewalls, implementation of data encryption, robust access control, etc. Additionally, data engineers should design the data systems in such a way that they comply with the rules of data privacy regulations.

Here are some important factors that ensure data protection and compliance:

  1. Robust data security: Implementing robust security measures to ensure the integrity and confidentiality of sensitive business information.
  2. Encryption: Encrypting data (not in use) and the ones that are about to be used to prevent unauthorized access.
  3. Access Controls: Setting up granular access permissions to limit who can view or modify data.
  4. Firewalls: Deploying firewalls to protect against external threats.
  5. Organizational Policies: Establishing clear guidelines for data handling, data storage, and data retrieval.

Additionally, data engineers should familiarize themselves with data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These regulations set standards for data protection, transparency, and user rights

Should Data Engineers be Hired as Experts on Demand?

As established, data engineers regularly handle critical data. However, it’s essential to recognize that each piece of data is specific to a particular project or task. When companies hire data engineers as experts on demand for specific projects, it ensures a good degree of data privacy for other ongoing projects in the pipeline. Additionally, when data engineers serve as experts on demand, they become part of the company’s inner circle for a finite period. This limited association prevents unintentional access to sensitive data meant for others.

Most importantly, hiring data engineers as ‘Experts on Demand’ grants companies access to specialized knowledge, optimal data strategies, and pipeline optimization. These advantages significantly benefit the company.

For data engineers themselves, this approach offers opportunities to work on diverse projects of varying scales and nature. They can also enjoy flexibility with their time—especially given the high-stakes nature of their work. After completing an extensive project, they may want to relax and unwind. Furthermore, their monetary remuneration can increase based on the complexity level of the project.

Learn how you can effectively scale your business through Experts on Demand.

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