Data Ingestion Framework: Streamlining Enterprise Data Pipelines

A data ingestion framework is essential for enterprises to enhance data movement and accessibility. The right ingestion framework facilitates the development of scalable data pipelines, reducing bottlenecks and optimizing workflows.

However, choosing an inappropriate data ingestion framework can lead to data silos, latency, and inefficiencies. To avoid these challenges, let’s explore the data ingestion framework in detail, its key components, and how to build a robust data ingestion pipeline that improves data flow across the enterprise.

What is a Data Ingestion Framework?

The data ingestion framework is a structured approach that simplifies the process of data collection from various sources. Having a framework for data ingestion provides a clear outline for transferring data from source to destination, increasing the efficiency of the process. After ingesting, you can clean and transform the data in a standardized format to store it in a centralized data system. You may then use this data further for analysis or other downstream operations.

The concept of data ingestion framework appears to be similar to data ingestion architecture. However, data ingestion architecture defines the overall structure of the data ingestion process, including how data will flow through different components. On the other hand, a data ingestion framework is a set of methods that you can use to perform data ingestion.

Components Of a Data Ingestion Framework

To design a data ingestion framework, you must first understand all its important components. Some of these are described below:

Sources

To start the data ingestion process, you should first identify all the relevant data sources from which you want to extract data. These sources can be databases, flat files, websites, APIs, or IoT devices. Identifying sources in advance ensures credibility and allows you to select the right set of tools for further data processing.

Transformation

The transformation component involves converting the extracted raw data into a consistent format before it is stored or analyzed. For transforming data, you can use various data cleaning techniques, such as handling missing values, removing duplicates, normalization, and aggregation.

Orchestration

Data orchestration is an automation method that involves managing the data pipeline-building process, of which data ingestion is an integral part. By orchestrating data pipelines, you can schedule and monitor how data moves across different components within your infrastructural setup. This eliminates the need for manual intervention to supervise data-based workflows, improving overall performance.

Destinations

The destination component refers to the data storage systems where you can store your ingested and transformed data. The prominent destinations are databases, data lakes, and data warehouses. Consolidating data in a destination system simplifies accessing data anytime for querying, analysis, and visualization.

Observability

After executing a data ingestion pipeline, you should regularly monitor its performance to detect failures and ensure data integrity. To accomplish this, you can use data observability techniques such as comparison, root cause analysis, or data lineage. You can also track metrics such as data ingestion rate, processing time per batch/job, and system resource utilization as they enable quick mitigation of performance issues.

Types Of Data Ingestion Frameworks

There are three main types of data ingestion frameworks:

Batch Ingestion

The batch data ingestion framework is suitable when you want to efficiently transfer large volumes of data. Using this framework, you can move data in batches at scheduled intervals on an hourly, daily, weekly, or monthly basis. You may also set up triggers to extract data in batches.

For example, an e-commerce company starts order processing only after the number of orders placed is 100. On reaching this number, the order data for different products is collected from the source databases and loaded into a data warehouse for further processing.

How Does Batch Data Ingestion Framework Work?

To start batch data ingestion, you need to configure a trigger or schedule a time at which you want to start extracting data. As soon as the predefined time or the trigger event occurs, the ingestion process will start. After ingestion, you can transform the data and load it to the destination of your choice for further usage.

Streaming Ingestion

Streaming data ingestion is a real-time process in which you can extract data from sources as soon as it is created. This technique is suitable for sources that generate data continuously, such as IoT-based sensors and time-sensitive applications like AQI monitoring or stock market trading.

How Does Streaming Data Ingestion Framework Work?

A streaming data ingestion framework enables you to continuously collect, process, and move real-time data from multiple sources to a destination. Tools like Apache Kafka act as a broker that facilitates the continuous collection and streaming of data. Stream processing engines like Apache Flink enable data processing and enrichment. Further, the processed data is stored in real-time databases. This framework ensures low-latency data availability for downstream applications.

Hybrid Ingestion

Hybrid ingestion is the combination of batch and streaming data ingestion. It is further categorized into two methods: Lambda architecture and Micro-batching. The Lambda architecture consists of three layers: speed, batch, and serving. The batch and serving layers help you perform batch data ingestion. On the other hand, the speed layer enables quick extraction of data that is not synced by the other two layers.

In the Micro-batching process, a server allows you to ingest data in smaller data batches at a higher frequency than the conventional batch processing. This approach bridges the gap between batch processing and real-time streaming. It is suitable if you want to extract data for near real-time operations.

How Does Hybrid Data Ingestion Framework Work?

Hybrid data ingestion involves two processes for data collection. The real-time data is collected via message brokers, while the batch data is extracted periodically from the relevant sources. You can then use a stream processor to standardize real-time data and a batch processor to streamline batch data. Once this is done, you can load the data to an appropriate target data system.

How to Choose a Data Ingestion Framework?

To choose a suitable data ingestion framework, you should follow these steps:

Identify End Objective

The first step is to identify the end use case for which you want to ingest data. It can be used for data analytics, creating reports for business intelligence, or developing AI/ML applications. You should also review the sources and time required for data collection, along with the storage options.

Choose an Ingestion Mode

Choose any available data ingestion modes, such as batch, streaming, or hybrid. Selecting the right ingestion approach helps you to fulfill all the data processing requirements, ensuring optimal performance.

Compare Features of Different Data Ingestion Frameworks

There are several data ingestion frameworks available, including Airbyte, Apache Kafka, Apache NiFi, AWS Kinesis, and Google Cloud Dataflow. Before selecting any of these options, you should compare the features such as speed, scalability, reliability, and deployment costs. This enables you to find a suitable solution that fits your organizational requirements and budget.

Test

After choosing a data ingestion framework, test it using methods such as prototyping and benchmarking. Prototyping involves conducting a simple data ingestion process with a subset of data for performance evaluation. On the other hand, benchmarking is the process of comparing the framework functionality based on metrics such as accuracy, throughput, and latency. You can then proceed to use the chosen framework to build the data ingestion pipeline.

Review

You should continuously monitor the performance of your selected data ingestion framework. Constant reviewing and documenting results enables you to ensure the quality of the data ingestion process. You can also share the results with other stakeholders, including the data engineering team, to foster transparency and accountability in handling data.

Data Ingestion Vs. Data Integration Vs. ETL

Sometimes, the terms data ingestion and data integration are used interchangeably, but they differ from one another. Let’s overview the definitions of data ingestion vs. data integration to understand the differences.‍

Data ingestion is the process of importing raw data from multiple sources into a storage data system. Data integration occurs after data ingestion, where the ingested data is processed, transformed, and consolidated into a unified format.

The data integration methods can be implemented using ETL or ELT approach. In the ETL integration, you extract data from various sources, transform it into a standardized format, and then load it to a target data system. On the other hand, in the ELT process, you can load the extracted data directly into a destination and then perform the necessary transformations.

The intent of data ingestion is to collect data for immediate use or storage. In contrast to this, the aim of data integration is to get a comprehensive view of all the organizational data. It helps you to gain a better understanding of how to perform data-related operations.

Build Vs. Buy Data Ingestion Framework

You may find that building a data ingestion pipeline can be a viable solution as it provides you with more control and customization capabilities. However, developing and maintaining a data ingestion framework on your own can be highly complex and time-consuming.

As an alternative, you can opt to buy a data ingestion framework. There are several low-code or no-code data ingestion tools available that can save time and monetary resources. One such solution is Airbyte. It is a robust data movement platform with an extensive library of 550+ pre-built connectors. You can use any of these connectors to extract data from sources and load it to a suitable destination.

For developing data ingestion pipelines, Airbyte offers multiple options, including UI, API, Teraform Provider, and PyAirbyte. During data ingestion, you can leverage the CDC feature to capture incremental changes made to the source data system. Replicating these changes in the destination allows you to maintain data consistency across systems while creating enterprise data pipelines.

Some prominent features of Airbyte are:

• Flexibility to Develop Custom Connectors: Airbyte provides several options to build custom connectors if the connector you want to use is not available. These options include Connector Builder, Low Code Connector Development Kit (CDK), Python CDK, and Java CDK.‍
• AI-powered Connector Development: While building custom connectors in Airbyte, you can leverage the AI assistant available in Connector Builder. The AI assistant helps you automatically pre-fill important connector configuration fields and offers intelligent suggestions to fine-tune the configuration process.‍
• Streamline GenAI Workflows: Airbyte allows you to ingest semi-structured and unstructured data, which you can further load into vector databases like Pinecone or Weaviate. You can then integrate these vector databases with LLMs to perform contextually accurate searches, enhancing GenAI workflows.‍
• Open-Source: The free-to-use open-source edition of Airbyte offers numerous features to build a flexible data pipeline. It allows you to migrate data from multiple sources into a preferred destination without vendor lock-in.‍
• Flexible Pricing: If you want to use advanced features, you can opt for the paid Airbyte Cloud, Team, and Enterprise editions. These versions support flexible pricing through the pay-as-you-go pricing model, facilitating the development of the data pipeline while managing costs.

Conclusion

The data ingestion framework is important for collecting and processing data for storage and analytics. This blog gives you a comprehensive overview of the data ingestion framework, along with its components and types. Depending on your resources and budget, you can buy or develop a data ingestion framework on your own. By selecting any one of these two approaches, you can ensure continuous data movement to gain maximum insights for improving business performance.