Research的动态

Most data teams are stuck in a loop. Analysts blame data engineers; data engineers point fingers at upstream developers. Everybody suffers. So how do we break out of this deadlock? 1. Remember, it's a team sport. 2. Capture business requirements from the customer itself. 3. Shift them left to circuit-break bad data before it spreads. Today, Soda makes this easier than ever. With its no-code interface, teams can give customers the power to define data quality checks that matter. The result? Less finger-pointing, more collaboration, and faster fixes. What’s been your biggest challenge in improving data quality across teams??

Most data teams are stuck in a loop. Analysts blame data engineers; data engineers point fingers at upstream developers. Everybody suffers. So how do we break out of this deadlock? 1. Remember, it's a team sport. 2. Capture business requirements from the customer itself. 3. Shift them left to circuit-break bad data before it spreads. Today, Soda makes this easier than ever. With its no-code interface, teams can give customers the power to define data quality checks that matter. The result? Less finger-pointing, more collaboration, and faster fixes. What’s been your biggest challenge in improving data quality across teams?

Most data problems come from gaps in team processes and priorities rather than technology. I've been on teams that skipped simple data quality checks, leading to bad insights. I’ve also worked on projects where pipelines were built without aligning with business needs, making them irrelevant. These aren’t technical issues. They’re cultural and process challenges. What I’ve learned is that teams need to focus on building trust in their data and improve how they work together to avoid these pitfalls and deliver real value. #dataengineering

Data quality is a regular practice of a successful data quality culture. The purpose of an organizational culture is to introduce regular practices that all team members follow without asking for direction and questioning the purpose. Here are my ideas for building a successful data quality culture. The most critical steps that must be done to achieve success: ?? Define standard metrics. ?? Focus on the reusability of data quality practices, code, and checks. ?? Train all team members in data quality. ?? Share the data quality results with business sponsors. #dataquality #dataengineering #datagovernance

I recently wrapped up a project that really showed me how essential Data Engineers are. We tackled a tough data integration issue. Here’s how it went down: Infrastructure Foundation. I built a data pipeline to keep things flowing smoothly. It became the backbone of our whole project. Data Quality Assurance. I added validation steps to ensure the data was reliable. Quality data means better decisions, plain and simple. Enhanced Efficiency. With automated workflows, we cut down on manual tasks. That freed up the team to focus on analysis, not prep work. Collaborative Insights. We all played to our strengths. I handled the engineering, while others focused on analysis and visuals. Together, we made a well-rounded team. Successful Impact. In the end, we got actionable insights that shaped business strategy. It was a win for the team and a reminder of the value Data Engineers bring. This project? It reinforced for me that Data Engineers are key to unlocking data’s potential.

Engaging with business teams isn’t enough for data engineers! To set up effective data flows, understanding how data is created and stored is critical. Build relationships with teams that generate data—not just those that use it. This ensures accurate data modeling, proactive issue resolution, and smoother change management. Data engineering shouldn't happen in a vacuum. Let's foster collaboration across the entire data lifecycle! ! #dataengineering #datamanagement #collaboration #DE #data

Many leaders of small and medium-sized companies believe that Data Engineering is reserved for large corporations. They couldn't be more wrong! ?? Discover how Data Engineering helps small and medium-sized businesses automate processes, enhance capabilities, integrate systems, and collaborate more efficiently. Dive deep with us for more details...

One of my teams goals this year is to invest in data quality. We've reached a good maturity level in our team's processes and projects, and now we want to consistently improve data quality for our customers ?? . Eden Nagar ?? is leading this initiative in the team, and this week we discussed a critical question: Who's responsible for validating the quality of our data pipeline sources? To provide context, our processes often rely on event data that we don't originally create and ingest - these are managed and ingested by other teams, and we simply use the output for our pipelines. We're talking about dozens, if not hundreds, of such data sources ???? . The discussion split into two perspectives: ·??????We can't check everything, so we shouldn't - this burden falls on the team ingesting the events. We should focus only on ensuring we have data ready in the sources and performing quality checks only on our own targets. ·??????To ensure end-to-end pipeline quality, we must validate everything, including sources before we start working with them. I personally lean towards the first approach, but I'm genuinely interested in hearing perspectives from both sides.

As technology becomes increasingly embedded in business operations, the data team’s responsibilities are expanding beyond traditional data management and engineering. This evolution demands a broader skill set such as incorporating system design, configuration comprehension, and a strategic approach to integrating technology into data workflows. Given this growing interdependence, how should the data team and system configuration work together? Should they sit together, collaborate more closely, or even merge into a unified team? Let me your thoughts???? #solideogloria

Successful data warehouse projects require a clear vision, full team collaboration, and strong communication. Following these steps ensures alignment with business goals and maximizes the return on investment. #DataWarehouseSuccess #Collaboration #Innovation