What is Data Ingestion: Best Practices & Compared With ETL

Data-driven insights are a crucial requirement for businesses looking to gain a competitive edge. The need for this stems from the requirement to understand and deliver services according to current trends and customer preferences. This is where data analytics comes into the picture. However, valuable data is often dispersed across various sources or remains in silos, complicating data analytics. Data ingestion can help consolidate this dispersed data into a unified form, which you can use to perform comprehensive data analysis.

This blog explains the data ingestion definition, compares it with ETL, and offers best practices for effective data ingestion to streamline data movement and enhance your analytical outcomes. 

What is Data Ingestion?

Data ingestion is the process of importing data from multiple sources to a single, centralized repository, such as a cloud-based data warehouse, data lake, or database. It allows you to gather data from various sources, such as databases, log files, spreadsheets, etc.

Data ingestion is the preliminary step in data integration, following which you can easily access the ingested data for transformation and integration within the centralized system. This helps break down data silos, unifying disparate data sets, helping you make data-driven decisions for your enterprise.

Benefits of Data Ingestion

There are several benefits of data ingestion, including:

• Centralized Data Access: Data ingestion consolidates diverse data into a centralized location, allowing easier access for your team members. By ingesting data, you can save considerable time and resources that would be otherwise required for gathering data. It also helps you eliminate data silos and foster a collaborative work culture within your organization.
• ‍Simplifies Data Integration: Ingestion streamlines data from disparate systems into a unified place. This readily available consolidated data can be efficiently transformed in the data integration process into a standard and consistent format for further analysis.
• ‍Provides Timely Insights: Data ingestion facilitates data aggregation and allows you to analyze various parameters that may affect your desired business outcome. This can help generate comprehensive and timely business insights essential for strategic decision-making.
• ‍Improves Productivity: By automating data collection, data ingestion facilitates quick preparation of reports and faster extraction of insights. This, in turn, helps you respond to customer needs and adapt to current market trends, boosting overall productivity.

Types of Data Ingestion 

Data ingestion can be broadly divided into three types:

Batch-based Data Ingestion

Batch-based data ingestion allows you to collect and transfer data in batches at scheduled intervals or based on trigger events. You can use batch-based data ingestion to collect data points hourly, daily, weekly, or monthly when you do not need data for real-time decision-making.

Real-time Data Ingestion

The real-time data ingestion process facilitates collecting and transferring data from source systems in real-time. Change Data Capture (CDC) is commonly used to capture any change in the source data and sync it with the destination database continuously. 

Real-time ingestion is essential for time-sensitive use cases, such as stock markets, where you must rapidly react to new information.

Hybrid Data Ingestion

Hybrid data ingestion enables you to combine the features of both real-time and batch data ingestion. The two most common hybrid methods are Lambda architecture-based and micro-batching. 

Lambda architecture-based data ingestion consists of speed, batch, and serving layers. The batch and serving layers perform batch-based data ingestion while the speed layer facilitates instant ingestion of data not synced by the other two layers. 

In micro-batching, a server performs a batch operation every few milliseconds or seconds. This method is useful when you want to process data in batches faster than the standard batch ingestion.

How does the Data Ingestion Process Work?

The data ingestion process consists of the following steps:

Step 1: Data Discovery 

It is the primary step of data ingestion, in which you need to explore different data sources. During data discovery, you should check factors such as where and how much data is available and in what way it can benefit your enterprise. 

Step 2: Data Acquisition

After identifying sources, you have to collect data from various sources and bring it into your system. The sources can be databases, CSV files, or web service data. This step can be slightly challenging as different sources have data in different formats. It's important to acknowledge that data from different sources might have inconsistencies in format or quality issues like missing values or inaccuracies. You can work on these issues during the transforming stage.

💡Suggested Read: Data Extraction Tools

Step 3: Data Validation

In this step, you should check the accuracy, completeness, and consistency of the acquired data.  Data validation involves applying various checks, such as data type validation, uniqueness validation, or range validation. These checks help to identify whether your data is in a standard format and what transformations you can perform to streamline analysis. 

Step 4: Data Transformation

The transformation process involves converting data into a desirable format that aligns with your analytics needs. It consists of several steps, such as normalization, aggregation, and standardization. 

Step 5: Data Loading

The last step involves loading the transformed data into your target system, which could be a data warehouse, data lake, or another analytical platform. This can be done through batch or real-time loading as per your requirement. You can then use this ingested data to gain useful insights through analysis. 

Challenges of Data Ingestion

Some of the challenges associated with the data ingestion process are:

Data Security

Ensuring the security of data throughout the data ingestion process is crucial. This involves implementing authentication, encryption, and authentication methods.

Schema Changes

When dealing with data ingestion on a large scale, it can become difficult to maintain consistency in the format or quality of data between source and destination applications. These issues often lead to performance challenges, affecting the data ingestion throughput.

Data Latency

When handling massive data volumes, the data transfer process slows down, resulting in backlogs. Also, the data you ingest from diverse sources may not be in a standard format and requires processing, affecting the speed of data movement. This results in data latency, further delaying the overall data analysis process.

Monitoring and Troubleshooting

Tracking the status of data across multiple sources and identifying issues during ingestion can be challenging. Inadequate monitoring and error handling can lead to data loss or duplication, resulting in data inconsistency and integrity issues. 

To overcome data ingestion challenges, you can implement some best practices mentioned in the below section.

Best Practices for Data Ingestion

Here are some of the data ingestion best practices to consider for maintaining data integrity and optimizing further data processing:

1. Choose the Right Data Ingestion Method

Based on the type and volume of your data, choose a suitable method for data ingestion. Your choice of batch ingestion or real-time ingestion depends on how quickly you need to process the data. Additionally, select data ingestion tools that are compatible with your data sources, target data platform, and budget.

While choosing the right method is essential, the data ingestion process can sometimes be complex. There are tools available to streamline this process, such as Airbyte, a data ingestion and replication tool that can help overcome the above-mentioned challenges. 

With its intuitive interface and versatile features, Airbyte can simplify your data ingestion process, allowing you to focus on valuable insights.

Airbyte offers 350+ pre-built connectors to build automated data pipelines. Access to such an extensive connector library can help you connect to various data sources and ingest data from them quickly. With its Connector Development Kit (CDK) feature, you can effectively build your own connectors with minimal code.

Some of the key features of Airbyte are:

• Change Data Capture: Airbyte supports Change Data Capture (CDC) to capture incremental changes in the data source and reflect them at the destination. It facilitates faster data ingestion for better decision-making. 
• ‍PyAirbyte: You can use the  Python library called PyAirbyte, which allows you to extract data from various sources supported by Airbyte. This simplifies the designing and maintaining of data pipelines within existing Python environments.
• ‍Schema Changes: Airbyte allows you to define how schema changes should be handled through connection settings. There are four options—propagate all changes, propagate column changes only, detect changes and manually approve, and detect changes and pause connection.
• ‍Monitoring Capabilities: The platform facilitates integration with Datadog, a monitoring and security platform for scrutinizing the health and performance of your data ingestion pipelines. This helps you identify and resolve errors during the ingestion process. 

2. Cleanse Your Data

Consider enhancing the quality of your data before performing data ingestion. This includes cleaning your data by correcting errors, removing duplicates, and filling in missing values to ensure the accuracy of data analysis outcomes.

3. Secure and Encrypt Your Data

Securing your data before ingestion is vital for protecting it from unauthorized access or tampering. Implement robust security measures such as authorization, authentication, firewall, etc., to restrict access to your data. You should also encrypt your data at rest and in transit to avoid data security breaches.

4. Make a Copy of Raw Data

Maintain a backup of your raw data before starting the ingestion process. This allows you to revert to the original data if you want to re-process it or if some discrepancies occur during the transformation process.

5. Implement Idempotency

An operation or function is said to be idempotent if it produces the same outcome on repetition. Idempotency in data ingestion ensures that you get the same output even after repeatedly importing data and prevents duplication in case of any error during the ingestion process.

6. Maintain Documentation

Maintain clear documentation of data sources, ingestion processes, and applied transformations. This helps in understanding and troubleshooting any issues arising during the process.

7. Use Automation

Utilize automated tools for data ingestion as they simplify the ingestion process. They also reduce the possibility of any data discrepancies or errors. Automation tools allow you to handle large volumes of data efficiently, which is not possible with the manual method.

Data Ingestion vs ETL: A Quick Comparison

While data ingestion and ETL (Extract, Transform, Load) might seem like closely related processes, there are some major differences between them, such as:

Conclusion

This article comprehensively explains the data ingestion meaning, challenges and best practices to enable you to initiate effective data analysis. Data ingestion facilitates the creation of unified datasets for analysis, modeling, and decision-making through streamlined collection and movement of data into centralized repositories. You can incorporate the best practices of data ingestion to effectively ingest your data and ensure it is optimized for use in future workflows.

FAQs

Why is monitoring data quality important before data ingestion?

Monitoring the quality of the data is essential to prevent unclean data from corrupting your clean data. A data quality check before ingestion also helps identify any discrepancies with the data source before using it for data analytics.

Which tools can you use for data ingestion?

There are many software that can be used as data ingestion tools. Some of these are Airbyte, Amazon Kinesis, Apache Kafka, Apache Nifi, and Apache Flume. 

What is data ingestion vs. data preparation?

Data ingestion is the initial step of collecting data from different sources into a central location. Data preparation takes over after the data is ingested and focuses on making it clean and usable for downstream applications.