How do you balance data quality and data velocity as a data engineer?

Both data quality and velocity are vital to ensure we provide business with trustable data at the right time. To provide quality, implement DQ checks (like row counts, referential integrity etc) at every ETL level to catch issues proactively rather than reacting. Also, implement best practices recommended by respective tools to build pipelines that are efficient, cost effective and optimized pipelines. Velocity is very subjective depending on the usecase and we have varieties of tech like Kafka for real time streaming and Databricks autoloader features for data lake etl to name a few. As per me, DQ takes precedence over velocity as we cannot really benefit with data when its quality is not trusted not matter how fast its available.

Data science is a versatile discipline that employs statistical methods, algorithms, and data analysis to derive valuable insights and understanding from extensive and intricate datasets. Data science plays a pivotal role in facilitating well-informed decision-making, addressing challenges, and fostering innovation in diverse sectors.

What is often missed in all the curriculums I've seen on Data Science is the ability to answer, seemingly, straightforward question: "What does this mean for our business?" Before all the modeling, even the fancy one, and definitely before any of the GenAI stuff, one has to make sure that the question is worth answering. Being able to communicate with business and understand their needs comes before a single line of code is written. And no project is worth anything until its results are properly communicated to the stakeholders. Then, and only then, your work has value. As for the ML part of it, I would advise starting with the simplest and most explainable algorithms to give yourself a benchmark. More often than not, it will be enough.

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves various techniques from statistics, data analysis, and machine learning to analyze and interpret complex data. Managing the rate of analysis vs the rate of data ingestion can be difficult but there are many tools that can be leveraged to improve on the data ingestion (ETL) as well as the analysis side of the data journey. Understanding how data can answer your questions is largely what a data scientist is responsible for. A basic understanding of SQL, Python and R can make someone more adept at processing and extracting insight more quickly.

Data Engineering is an art of getting the right data in the right format with the right quality on time to feed the business. It's a rarity to start as a Data Engineer, usually, people turn DE from related areas, such as data analytics, DBA or SWE - lots of transferrable knowledge there. Things you really need: - RDBMS understanding - SQL proficiency - Python Things that you need, but don't realize: - Data modelling - Architecture - MPP systems It is easy to get lost in the myriad of new tools, frameworks and solutions, with more coming each day. Fundamentals of data handling and modelling remains the same. And SQL - it will outlive you, don't believe anyone saying otherwise. Read "Fundamentals of Data Engineering" by Reis & Housley.

Data engineering involves designing and maintaining data pipelines and infrastructure to collect, store, and prepare data for analysis. It focuses on data's reliability, scalability, and efficiency, ensuring it's readily accessible for data scientists and analysts.

Data engineering involves building pipelines that extract data from various sources, transforming it into a usable format, and loading it into storage or analytical systems. Data engineering is continuously evolving with tons of options available to solve any use case.

Data engineering is the aspect of data science that focuses on practical applications of data collection and data processing. It involves the design and construction of systems for collecting, storing, and analyzing data at scale. Data engineers build and maintain the infrastructure and tools that allow data scientists to conduct their analyses.

"Data engineering involves constructing and managing infrastructure and pipelines to support data science. Engineers collect, store, process, and deliver data from diverse sources like databases and APIs. This role demands programming, engineering, and scalability skills."

Part of being a data engineer for a data sharing platform is to be not only reactive, but also proactive. Find a way to configure usage monitoring, common access patterns and, more generally, the ways in which data is used. Knowing that, you can optimize the performance to better match the needs of DS team. Moreover, you may spot a potential for a Data Product to offer. If the same datasets are joined in a same manner, cleaned and\or enriched in the same manner, you can save DS team a lot of time by moving these operations upstream. And remember, your analytics is only as good as your data. Unless you have dedicated people and tools to monitor data quality, it is a job for DE team. Providing DS team with quality data benefits everyone!

Data engineering and data science are complementary, with data engineering providing the foundation and tools needed for data science activities. Data engineers prepare and make data accessible, while data scientists analyze that data to generate insights. Effective collaboration between the two ensures that data is both useful and used efficiently. Think of it as a relationship between a builder and a interior designer. The builder is responsible for providing the structure in which the designer is able to move and shape the internal environment. This is similar with the data engineer and a data scientist. One is responsible for providing the structure of the data so it can be analyzed, the other is repsonsible for extracting insights.

"Data science and data engineering collaborate closely, each fulfilling vital roles in the data lifecycle. Data engineering ensures data reliability and accessibility for data science endeavors, while data science defines data needs and use cases for engineering. Together, they transform data into actionable insights, enabling informed decision-making."

A key challenge, particular to data velocity, is poorly defined requirements often being a part of the process. The level of urgency to incorporate new data sources must be balanced with firm requirements on the intended use case, attributes and context of the data itself, and any supporting architecture or ERD diagrams to not place a Data Engineer in a position of "just figure it out".

A challenge I face in every project is managing large volumes of data efficiently, ensuring data quality and reliability, dealing with data security and compliance, and scaling infrastructure to accommodate growing data needs. Also, I think data engineers must stay updated with evolving technologies and tools in a rapidly changing field.

If your company does not have a data architect, data engineer has to become one. If it is a greenfield project, the world is your oyster (provided, you can fit that oyster into a given budget and security restrictions). Even then you'd have to work tightly with business to learn what are the exact requirements from the data - it is challenging, because you cannot expect business to speak engineering, you have to understand business instead. And if you are in charge of an already existing infrastructure and tooling, the biggest challenge is to be honest with yourself and understand when technology needs to change and when you're required to adapt. Don't let Resume Driven Development throw a wrench in a perfectly functioning system!

While it's easy to build data pipelines, it get's challenging to build the same pipelines with below characteristics. 1) Can scale up/down as per data volume. 2) Resilient to changes ( Schema evolutions). 3) Cost Effective ( consumes less i/o and makes efficient use of computer power). 4) Robust 5) Data Quality checks (Intrusive and Non-Intrusive). 6) Platform agnostic ( due to frequent migration needs as new tools become available). It pays off both in short and long term when effort is paid to build pipeline with these characteristics.

The term "Data Velocity" emerged with the advent of big data, focusing on the speed at which data is generated and collected. However, the current discussion revolves around data quality and timeliness. Data quality requirements and metrics are established by data owners and implemented by engineers as data quality rules and controls. Timeliness is a crucial aspect of data quality, as defined by data owners to ensure data is fit for its intended purpose. The acceptable levels of completeness, accuracy etc. are weighed against the timeliness of data delivery, which depends on specific KPIs and use cases. Data owners are responsible for setting thresholds for incomplete or inaccurate data versus the availability (timeliness) of the data.

Being a data engineer, I feel data quality and data velocity are two important factors to consider before we share the data to the end users. Data Quality standards and guidelines must be well discussed in advance, ensuring appropriate measures are taken withholding enough testing, validation and automation. Equally data velocity ensures timeliness insights of data with proper integration approach keep the solutions efficient, and scalable enough to make real time decisions. Collaboration amongst various data teams from business and other stakeholders play a crucial role to adhere to these standards maintaining perfect balance between high quality fata and its velocity for any data project.

I think it's totally dependent on the business case here & the level of tolerance of having the data more often or more accurate. In some business cases, stakeholders just need to get the information right away to have a general conclusion about a specific issue/topic. On the other hand, they don't mind to wait till next day to get an accurate figures to have a reliable trend analysis for example. The bottom line here, that you can't always "Balance" Data Quality vs Velocity. But, you can play with them to achieve the inquired Business Value.

Data quality is checks. It is a balancing act between the amount of checks, frequency of checks and time it takes to run them. In an ideal world, you would run checks after each step of your ELT\ETL process to verify that the step produced the expected result. If something goes sideways (and it will), it will be easier to find the exact reason. Checks can be technical - validating number of rows and so on, and business - rules defined by business, based on domain knowledge. Trade-off to testing everything, everywhere, all the time is the speed and compute resources. It is important to track business rules and monitor pipeline performances, but learn to prioritize things business do. Have well-defined mitigation and escalation plans.

I would say to balance data quality and velocity as a data engineer: 1. **Quality Assurance**: Implement validation and cleansing processes. 2. **Structured Flow**: Optimize data pipelines for efficiency. 3. **Streaming vs. Batch**: Choose based on use case. 4. **Monitoring**: Track data quality and pipeline performance. 5. **Governance**: Define ownership and policies. 6. **Automation**: Automate quality checks and scaling. 7. **Feedback**: Collaborate with users for improvements. 8. **Archiving**: Manage data retention for cost-effectiveness. 9. **Catalogs**: Use metadata to aid data discovery. 10. **Resource Optimization**: Continuously adjust resource allocation. Balancing these elements ensures high-quality, fast-access data.

Balancing data quality and data velocity yields improved decision-making by ensuring accurate and reliable insights in real-time. It reduces errors, enhances trust in analytics, and supports data-driven strategies. This equilibrium optimizes resource utilization and helps organizations remain agile in a competitive landscape.

In my experience data engineers should concentrate on combining diverse data sources to render the data useful. As Jeff Bezos famously said, "you don't need perfect data," so the emphasis should be on progress and speed, rather than perfection. Data scientists should frame the modeling process, define the data exploration framework, and determine whether it's a classification or prediction problem. It is ok to have 60-70% of required data initially, by iterating frequency, the team can refine the model to near-readiness within weeks. This approach ensures there is a fine balance of quality and speed. I have written an article a related article.. https://www.dhirubhai.net/pulse/delivering-analytics-strategic-service-ramabhadran-kapistalam

Data quality and data velocity are two sides of the same coin. Too much emphasis on one can lead to bad results. Here are a few tips for balancing the two: - Define your requirements. What level of data quality and data velocity do you need for your specific use case? - Use data quality tools and processes. - Monitor your data. - Collaborate with stakeholders. By balancing data quality and data velocity, you can make better decisions and improve your bottom line.

Balancing data quality and data velocity as a data engineer involves establishing a data quality framework that includes validation, profiling and cleansing processes. Automation tools and data pipelines can speed up data processing while minimizing human errors. Additionally, clear communication channels between data engineers, scientists and business stakeholders are critical. Establishing a balance between data quality and velocity requires careful planning, effective implementation, and ongoing optimization to ensure data is accurate and accessible for informed decision-making.

One consideration when balancing data quality and data velocity is ensuring you have a business case for the data you're collecting. I've seen teams collect unneeded data and spend time refining that data for an unknown, future use case. Focusing only on the data you need for specific use cases saves time and effort. Also, it ensures you're not creating additional privacy or security liability by storing data you don't need.

Continuous Monitoring and Improvement: Data ecosystems are dynamic. Continuous monitoring of both data quality and processing speeds is essential, with regular adjustments as necessary. Education and Training: Ensuring that team members understand the importance of both data quality and velocity, and are trained in best practices for achieving balance. Technology Investment: Investing in the right tools and technologies that can help automate quality checks, streamline data processing, and support real-time data analytics. Data Governance: Establishing strong data governance policies is crucial for maintaining data quality over time. This includes defining clear roles, responsibilities, and processes for data management.