Type of Data Ingestion
Different Data Ingestion Types, including real-time, batch, and combination, were designed based on the IT infrastructure and business needs. Among the techniques for data intake are:
1. Real-Time Data Ingestion
Real-Time Data Ingestion
Real-Time Data Ingestion is the process of collecting and sending data from source systems in real-time solutions like Change Data Capture (CDC). One of the most popular types of data intake, particularly for streaming services, is this one. CDC transports updated data and redoes logs while continually keeping an eye on transactions, all without attempting to impede database activity. For time-sensitive use cases where organizations must respond fast to fresh data, like stock market trading or power grid tracking, real-time ingestion is essential. Additionally, in order to define and act upon new insights and make operational decisions fast, real-time data pipelines are required. Real-time data intake involves the extraction, processing, and archiving of data as soon as it is created to facilitate prompt decision-making.
2. Batch-Based data ingestion
Batch-Based data ingestion
Batch-based data ingestion is the practice of gathering and sending data in batches at regular intervals. For repeated procedures, data ingested in batches has the advantage of being transported at regularly scheduled periods. The ingestion layer can gather data using batch-based data intake types according to trigger events, basic schedules, or any other logical ordering. Batch-based ingestion becomes advantageous when an organization needs to gather particular data points on a daily basis or just does not need data for making decisions in real time.
3. Micro batching
Micro-batching is a data ingestion technique that falls between real-time and batch-based approaches. It involves collecting and processing data in small, predefined batches at regular intervals, typically ranging from milliseconds to seconds. This approach combines the advantages of both real-time and batch processing while addressing some of their limitations.
In micro-batching, data is collected continuously, but instead of processing individual events instantaneously, they are grouped into small batches before processing. This allows for more efficient resource utilization compared to processing each event in real-time. At the same time, it offers lower latency compared to traditional batch processing, as the processing intervals are much shorter.
What is Data Ingestion?
The process of gathering, managing, and utilizing data efficiently is important for organizations aiming to thrive in a competitive landscape. Data ingestion plays a foundational step in the data processing pipeline. It involves the seamless importation, transfer, or loading of raw data from diverse external sources into a centralized system or storage infrastructure, where it awaits further processing and analysis.
In this guide, we will discuss the process of data ingestion, its significance in modern data architectures, the steps involved in its execution, and the challenges it poses to businesses.
Table of Content
- What is Data Ingestion?
- Why Data Ingestion is Important?
- Type of Data Ingestion
- 1. Real-Time Data Ingestion
- 2. Batch-Based data ingestion
- 3. Micro batching
- The Complete Process of Data Ingestion
- Step 1: Data Collection
- Step 2: Data Transformation
- Step 3: Data Loading
- The Data Ingestion Workflow
- Challenges in Data Ingestion
- Benefits of Data Ingestion
- Data Ingestion vs ETL
- Conclusion