Data Pipelines

What is Data ingestion?

Data ingestion is the process of importing, transferring, or loading data from various sources into a target data storage system, such as a data warehouse, data lake, or database. Data can be structured or unstructured. It involves collecting and processing data from multiple sources, such as sensors, social media, websites, mobile devices, or other data sources, and transforming it into a structured format that can be analysed and used for business insights, reporting, and decision-making.

The first step in an effective data ingestion process is to prioritise the data sources. Individual files must be validated and data items routed to the correct destinations.

Data can be streamed in real time or ingested in batches.

Real-time: In real-time data ingestion, each data item is imported as the source emits it.

Batch: When data is ingested in batches, data items are imported in discrete chunks at periodic intervals of time.

There are several types of data ingestion mechanisms. Some of the commonly used ones are:

1. Batch Ingestion: In batch ingestion, data is collected over a period of time, and then processed and loaded in bulk. This involves processing data in large volumes, typically on a schedule or at regular intervals.
2. Real-time Ingestion: This involves streaming data from various sources in real-time, allowing for faster processing and analysis.
3. API-based ingestion: This involves using APIs to extract data from external systems(Various sources such as web services or social media platforms) and ingest it into a data store for processing and analysis.
4. File-based Ingestion: File-based ingestion involves processing/extracted the data from files generated by applications, devices, or systems. It is useful for batch processing of large volumes of data.
5. Stream Ingestion: Stream ingestion involves processing data as it is generated and ingesting it into a target system. It is useful for applications that require real-time processing and analysis of data.
6. Log-based Ingestion: Log-based ingestion involves collecting and processing logs generated by applications, devices, or systems. It is used for troubleshooting, monitoring, and performance analysis.
7. Database replication: This involves copying data from one or more databases to a destination database for analysis.

Overall, the type of data ingestion used will depend on the specific needs of the organisation and the types of data sources such as the type of data being ingested, the frequency of ingestion, the volume of data, the source/destination system.

What are the business benefits of data ingestion?

1. Better decision-making: Data ingestion helps businesses to collect and analyse data from various sources. This helps them to make better decisions based on real-time data and insights.
2. Simplicity. Data ingestion, especially when combined with extract, transform and load (ETL) processes, restructures enterprise data to predefined formats and makes it easier to use.
3. Availability. Efficient data ingestion helps businesses provide data and data analytics faster to authorised users. It also makes data available to applications that require real-time data.
4. Improved customer experience: With data ingestion, businesses can collect customer data and analyse it to understand their preferences and behaviour. This enables them to personalise their offerings and provide a better customer experience.
5. Increased operational efficiency: Data ingestion helps businesses to automate data collection and processing, reducing manual effort and increasing efficiency. This can result in cost savings and faster turnaround times.
6. Better risk management: With data ingestion, businesses can identify potential risks and take preventive measures to mitigate them. This helps to minimise the impact of risks and protect the business from potential losses.
7. Improved compliance: Data ingestion helps businesses to ensure compliance with data privacy and security regulations by providing better control over data collection, processing, and storage. This can help to avoid regulatory fines and penalties.
8. A simplified process of collecting and cleansing data imported from hundreds of sources, with dozens of types and schemas, into a single, consistent format.

Challenges of data ingestion and big data sets

Data ingestion also poses challenges to the data analytics process, including the following:

• Scale. When dealing with data ingestion on a large scale, it can be difficult to ensure data quality and ensure the data conforms to the format and structure the destination application requires. Large-scale data ingestion can also suffer from performance challenges.
• Security. Data is typically staged at multiple points in the data ingestion pipeline, increasing its exposure and making it vulnerable to security breaches.
• Fragmentation and data integration. Different business units ingesting data from the same sources may end up duplicating one another’s efforts. It can also be difficult to integrate data from many different third-party sources into the same data pipeline.
• Data quality. Maintaining data quality and completeness during data ingestion is a challenge. Checking data quality must be part of the ingestion process to enable accurate and useful analytics.
• Costs. As data volumes grow, businesses may need to expand their storage systems and servers, adding to overall data ingestion costs. In addition, complying with data security regulations adds complexity to the process and can raise data ingestion costs.

Data ingestion vs. ETL

Data ingestion and ETL are similar processes with different goals.

Data ingestion is a broad term that refers to the many ways data is sourced and manipulated for use or storage. It is the process of collecting data from a variety of sources and preparing it for an application that requires it to be in a certain format or of a certain quality level. In data ingestion, the data sources are typically not associated with the destination.

Extract, transform and load is a more specific process that relates to data preparation for data warehouses and data lakes. ETL is used when businesses retrieve and extract data from one or more sources and transform it for long-term storage in a data warehouse or data lake. The intention often is to use the data for BI, reporting and analytics.