Category: Data Engineering
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Revolutionizing Data Engineering: The Zero ETL Movement
Imagine you’re a chef running a bustling restaurant. In the traditional world of data (or in this case, food), you’d order ingredients from various suppliers, wait for deliveries, sort through shipments, and prep everything before you can even start cooking. It’s time-consuming, prone to errors, and by the time the dish reaches your customers, those…
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The Modern Data Stack: Still Too Complicated
In the quest to make data-driven decisions, what seems like a straightforward process of moving data from source systems to a central analytical workspace often explodes in complexity and overhead. This post explores why the modern data stack remains too complicated and how various tools and services attempt to address these challenges today.
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Simplify your Data Engineering Process with Datastream for BigQuery
Datastream for BigQuery simplifies and automates the tedious aspects of traditional data engineering. This serverless change data capture (CDC) replication service seamlessly replicates your application database to BigQuery, particularly for supported databases with moderate data volumes.
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The Problems with Data Warehousing for Modern Analytics
Cloud data warehouses have become the cornerstone of modern data analytics stacks, providing a centralized repository for storing and efficiently querying data from multiple sources. They offer a rich ecosystem of integrated data apps, enabling seamless team collaboration. However, as data analytics has evolved, cloud data warehouses have become expensive and slow. In this post,…
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How to Export Data from MySQL to Parquet with DuckDB
In this post, I will guide you through the process of using DuckDB to seamlessly transfer data from a MySQL database to a Parquet file, highlighting its advantages over the traditional Pandas-based approach.
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The Reality of Self-Service Reporting in Embedded BI Tools
Offering the feature for end-users to create their own reports in an app sounds innovative, but it often turns out to be impractical. While this approach aims to give users more control and reduce the workload for developers, it usually ends up being too complex for non-technical users who find themselves lost in the data,…
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Unlocking Real-Time Data with Webhooks: A Practical Guide for Streamlining Data Flows
Webhooks are like the internet’s way of sending instant updates between apps. Think of them as automatic phone calls between software, letting each other know when something new happens. For people working with data, this means getting the latest information without having to constantly check for it. But, setting them up can be challenging. This…
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Effortless Python Automation: Simple Script Scheduling Solutions
If you want your Python script to run daily, it might seem as simple as setting a time and starting it. However, it’s not that straightforward as most Python environments lack built-in scheduling features. There’s a range of advice out there, with common suggestions often involving complex cloud services, which are overkill for simple tasks.…
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Solving Pandas Memory Issues: When to Switch to Apache Spark or DuckDB
Data Engineers often face the challenge of Jupyter Notebooks crashing when loading large datasets into Pandas DataFrames. This problem signals a need to explore alternatives to Pandas for data processing. While common solutions like processing data in chunks or using Apache Spark exist, they come with their own complexities. In this post, we’ll examine these…
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From JSON Snippets to PySpark: Simplifying Schema Generation in Data Pipelines
When managing data pipelines, there’s this crucial step that can’t be overlooked: defining a PySpark schema upfront. It’s a safeguard to ensure every new batch of data lands consistently. But if you’ve ever wrestled with creating Spark schemas manually, especially for those intricate JSON datasets, you know that it’s challenging and time-consuming. In this post,…