Big data pipelines, like standard data pipelines, are the processes that help move data between the components of a data platform, filter, enrich, and transform it into shareable formats.
A big data pipeline differs from a standard one because of the need to process massive data volumes. When systems need to effectively handle petabytes of data, traditional pipelines can become unreliable and increase the risk of downtime.
To address these challenges, big data pipeline management emphasizes four key features.
In the last decade, most industries have doubled down on data collection tools and strategies, allowing them to tap into large amounts of customer, product, and market data. Building a big data pipeline to unlock the total value of these insights helps innovative companies in all fields drive change and stay ahead of emerging trends.
FAQ
The stages in a big data pipeline are ingestion, transformation, storage, and analysis.
To create a big data pipeline architecture, you need to define the data sources, data flow, and processing steps. It is also important to choose the tools for your tech stack that would support big data processing.
The five phases of big data analysis are data acquisition, data preparation, data exploration, data modeling, and data interpretation.
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