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10 Data Engineering Topics and Trends You Need to Know in 2024

ODSC - Open Data Science

Now that we’re in 2024, it’s important to remember that data engineering is a critical discipline for any organization that wants to make the most of its data. These data professionals are responsible for building and maintaining the infrastructure that allows organizations to collect, store, process, and analyze data.

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Best Data Engineering Tools Every Engineer Should Know

Pickl AI

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines. Thats where data engineering tools come in!

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Data observability: The missing piece in your data integration puzzle

IBM Journey to AI blog

Historically, data engineers have often prioritized building data pipelines over comprehensive monitoring and alerting. Delivering projects on time and within budget often took precedence over long-term data health. Better data observability unveils the bigger picture.

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Data Observability Tools and Its Key Applications

Pickl AI

Data Observability and Data Quality are two key aspects of data management. The focus of this blog is going to be on Data Observability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.

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What Is Data Observability and Why You Need It?

Precisely

It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may prompt you to rethink your data observability strategy. Complexity leads to risk. Learn more here.

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Highlights from the Data Engineering Summit Now Available On Demand

ODSC - Open Data Science

We’ve just wrapped up our first-ever Data Engineering Summit. If you weren’t able to make it, don’t worry, you can watch the sessions on-demand and keep up-to-date on essential data engineering tools and skills. It will cover why data observability matters and the tactics you can use to address it today.

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6 benefits of data lineage for financial services

IBM Journey to AI blog

But with automated lineage from MANTA, financial organizations have seen as much as a 40% increase in engineering teams’ productivity after adopting lineage. Increased data pipeline observability As discussed above, there are countless threats to your organization’s bottom line.