Data Ingestion in Databricks

When we talk about data engineering, we’re really talking about a journey, one that takes data from its rawest form and transforms it into something useful. This journey, known as the Data Engineering Lifecycle, happens in five key stages: generation, storage, ingestion, transformation, and serving.

In this blog, we’re zooming in on one crucial part of that journey: data ingestion

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So, what exactly is Data Ingestion?

Think of it as the process of moving data from where it lives (the source) to where it needs to be (the destination), so it can be stored, accessed, processed, or analysed later on.

Your source could be anything from SaaS tools, custom-built apps, and IoT devices to good old spreadsheets, databases, or files like CSVs and JSON. On the other end, your destination might be a data warehouse, a data lake, or a data mart: basically, wherever your data needs to land to start being useful.

Simple, right? Let’s break it down even more in the next section.

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Why Is Data Ingestion So Important?

Data ingestion is the backbone of the entire pipeline because it makes the data available for use. Some of the benefits of the data ingestion stage include: 

• Data Availability: Data ingestion makes data readily available for users. It allows the staging of data into a central location and this saves time that  engineers would have spent performing different operations and tasks to get data ready of  any analytical work 
• Data Uniformity and Quality: Data from different sources are of different formats and structures, and this makes it hard to further process the data. With data ingestion, data can be cleaned and validated, making sure that data meets  specified quality  standards.
• Data Analytics and Insights: Data ingestion allows businesses to derive insights from data by performing advanced analytics on the data for businesses to make better and smarter decisions.

Types of Data Ingestion

The method of data ingestion used is based on the type of data received which depends on the nature of the business operations and ecosystem. Some of the types of data ingestion are:

Batch Data Ingestion

Batch ingestion is the process of  collecting data from multiple sources into one location over a specific period of time and processing it in at once. So the name batch means either the data is scheduled to occur automatically or can be triggered by a user or an application Batch processing is easy and simple to implement. It also costs less in its implementation and has a minimal impact on system performance.

Real-time ingestion 

Real-time ingestion is the process of moving data continuously into a single location in real time. Once data is produced, it is ingested without delay. This method  is used by time-sensitive applications and offers faster insights and for  instantaneous decision-making. This type of ingestion is more expensive since it requires systems to constantly monitor sources and accept new data continuously. 

Lambda architecture

This type of data ingestion method combines both batch and real-time processing. It combines the strengths of each to provide a comprehensive solution for data ingestion. 

Challenges in data ingestion

In the process of trying to ingest data into a warehouse and build a pipeline, there are some challenges one can face.

• Data security: Data is an important asset for every company, so it needs to be protected at all costs. One must make sure data is not leaked or exposed to any third party, so in order to ensure data security regulations, extra complexity and cost are added.
• Scale and variety: Data will forever grow and increase in this age, and this can cause some serious performance issues as data increases within the enterprise or business. Scalability in this situation might become an issue.
• Data fragmentation: Different sources of data can create inconsistency and can prevent the generation of further insight derived from data analysis when the data is more unified. Data can be duplicated and fragmented just because they are coming from different sources.
• Data quality assurance: Data can be compromised at any point due to the complexity of the pipeline. When data starts moving through the pipeline, it no longer becomes a single entity again because of the operation being performed on it and easily affects the quality of the data. 

Conclusion

Data ingestion is a critical stage in the data engineering lifecycle, acting as the bridge between raw data sources and meaningful insights. By ensuring data is readily available, uniform, and of high quality, businesses can unlock the full potential of their data assets. Adopting the right data ingestion strategy—whether batch, real-time, or a combination—can set the foundation for robust data pipelines and smarter decision-making.

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