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According to Statista, global data creation is predicted to surpass 180 zettabytes by 2025. Data today is no longer confined to a single source or location. It is generated from many different sources and platforms spread across the globe.
Data is generated and stored in various environments, from cloud-based applications to on-premises systems. This multi-source data generation adds another layer of complexity as organizations must ingest, integrate, and analyze data from multiple locations to gain a holistic view of their operations.
This brings us to the question: what is data ingestion in big data and how is it used in the real world? This blog comprehensively looks into data ingestion, its types and use cases.
What is data ingestion in big data?
Data ingestion is the process of collecting and importing raw data from diverse sources into a centralized storage or processing system (a database, data mart or data warehouse). Ingestion facilitates data analysis, storage, and further utilization of data for decision-making and insight gathering.
Data ingestion in big data environments is different from regular data ingestion in traditional data systems. The key differences lie in the scale, complexity, and processing methods involved. In traditional data ingestion, structured data is typically extracted from sources such as ERP systems or relational databases, subsequently loaded into a data warehouse.
In contrast, big data ingestion involves a diverse range of sources and targets, incorporating streaming data, semi-structured, and unstructured data from IoT devices, server monitoring, and app analytics events. The variations in data types and ingestion techniques make big data environments more dynamic and challenging to manage effectively.
How to ingest data?
Ingesting data involves the process of importing, collecting, or loading data from various sources into a data storage system for further processing and analysis. Here are the general steps involved in ingestion:
- Data extraction: Retrieving data from various sources in its raw form. This can be achieved through APIs, file transfers, database queries, or web scraping.
- Data transportation: Moving the extracted data from its source to a centralized location or storage system, such as a data warehouse, data lake, or streaming platform.
- Data transformation: Preparing and cleaning the data to ensure it is in a consistent and usable format. This may include data validation, enrichment, normalization, and other data cleansing operations.
- Data loading: Storing the transformed data in the target data storage system, making it accessible for analysis, reporting, and other data processing tasks.
Understanding what is ingestion in big data also helps identify potential bottlenecks or inefficiencies in the data flow, allowing organizations to optimize their data pipelines.
What are the different types of data ingestion?
Data is the backbone of modern businesses which drives innovative solutions. However, to harness the full potential of data, it must be collected and processed efficiently.
This is where data ingestion comes into play. To truly understand what is ingestion in big data, one must be aware of its different types. There are various types of data ingestion each suited for different data sources and use cases, such as:
1. Batch data ingestion
Batch data ingestion involves collecting and processing data in predetermined, fixed-size batches. Data is accumulated over a specific period and then processed together. This method is particularly useful for non-time-sensitive data and scenarios where data consistency is important. Batch processing can be resource efficient as it allows data to be processed in bulk, making it ideal for handling large volumes of data.
2. Real-time data ingestion
Real-time data ingestion, as the name suggests, enables immediate data processing as soon as it becomes available. This method is ideal for time-critical applications like real-time analytics and monitoring. Real-time data ingestion ensures organizations respond promptly to changing data and make decisions based on the most up-to-date information.
3. Change data capture (CDC)
Change Data Capture (CDC) is a specialized data ingestion technique that focuses on capturing and replicating only the changes made to the source data. Instead of processing the entire dataset, CDC identifies the modifications (inserts, updates, or deletes) and extracts only those changes, reducing processing overhead. CDC is commonly used in databases and data warehouses to keep replica systems in sync with the source.
4. Streaming data ingestion
Streaming data ingestion deals with continuous, real-time data flow from various sources. It is especially suitable for data generated by IoT devices, social media feeds, sensors, and other sources where data is produced continuously. Streaming data ingestion platforms handle data in small chunks, processing and storing it as it arrives, enabling real-time analysis and insights.
5. Cloud data ingestion
Cloud data ingestion involves moving data from on-premises systems to cloud-based storage or processing platforms. Cloud-based solutions offer scalability and help reduce expenditures, making them increasingly popular for managing vast data. Cloud data ingestion allows organizations to leverage cloud resources and use various cloud-based data processing and analytics tools.
6. Hybrid data ingestion
Hybrid data ingestion combines elements of both on-premises and cloud-based data ingestion. Organizations utilize local and cloud resources to manage data ingestion, storage, and processing in this approach. Hybrid data ingestion is suitable for scenarios where certain data must remain on-premises due to regulatory or security requirements, while other data benefits from cloud-based scalability and services.
7. Lambda-based data ingestion
Lambda-based data ingestion is an architectural pattern that blends batch and real-time data processing. It involves processing data in two parallel paths: one for real-time data streams and the other for historical data processing in batches. This approach allows organizations to derive insights from real-time and historical data, providing a comprehensive view for analysis.
Top data ingestion tools for creating an effective data strategy
Efficient data ingestion is the foundation of a successful data-driven strategy. However, handling diverse data sources and formats can be challenging without the right tools.
By using effective tools for data ingestion, organizations can streamline the process of collecting and processing data from diverse sources. Some examples of useful tools for data ingestion are as follows:
Apache Kafka
Apache Kafka is a popular distributed streaming platform for real-time data feeds. It excels at processing high volumes of data from multiple sources and distributing it to various consumers in real time.
Kafka’s key strength lies in its fault tolerance, and low-latency capabilities, making it ideal for use cases like log aggregation, event streaming, and real-time analytics. Its pub-sub model allows data to be ingested and delivered to multiple systems simultaneously, ensuring efficient data flow across the organization.
Apache NiFi
Apache NiFi is an open-source data integration tool with a user-friendly interface for designing data flow processes. It simplifies data ingestion by supporting various data sources, including databases, IoT devices, social media, etc.
NiFi’s visual drag-and-drop interface allows users to create complex data pipelines without writing code. It also offers data provenance, security, and extensive monitoring capabilities, making it a robust choice for data ingestion in batch and real-time scenarios.
AWS Data Pipeline
AWS Data Pipeline is a fully managed service provided by Amazon Web Services (AWS) that facilitates the scheduling, execution, and management of data workflows.
It enables users to move data seamlessly between AWS services and on-premises data sources. With AWS Data Pipeline, users can design complex data ingestion workflows and benefit from reliable and scalable data transfer capabilities. The service also supports data transformation and processing tasks, making it a comprehensive solution for handling data ingestion in the AWS ecosystem.
Azure Data Factory
Microsoft’s Azure Data Factory is a cloud-based data integration service that enables organizations to create, schedule, and manage data pipelines across various data stores and platforms. Azure Data Factory provides:
- Robust data movement capabilities.
- Data transformation through Azure Data Flows.
- Seamless integration with other Azure services like Azure Databricks and Azure Synapse Analytics.
Moreover, it supports hybrid data ingestion, connecting users to on-premises and cloud-based data sources.
Google Cloud Dataflow
Google Cloud Dataflow is a serverless data processing service offered by Google Cloud Platform (GCP). It enables data ingestion and batch or stream processing using Apache Beam, an open-source unified programming model.
Dataflow offers autoscaling capabilities, ensuring optimal resource utilization and cost efficiency. With Dataflow, users can process data in real-time or batches, making it suitable for a wide range of data ingestion and processing scenarios on the GCP platform.
Use cases of data ingestion
Organizations can derive meaningful insights and make informed decisions by understanding how data ingestion is applied across various domains. Some of the real-world applications of data ingestion include:
- E-commerce order processing: Data ingestion in e-commerce is essential for tracking and analyzing customer orders, product sales, and inventory levels. It captures customer interactions, purchase history, and preferences to enable personalization and targeted marketing efforts.
- Social media analytics: In social media analytics, data ingestion entails collecting data from platforms like Twitter, Facebook, Instagram, etc. This data then analyzes user sentiments, trends, and brand mentions. Metrics such as likes, comments, and shares are gathered to measure the success of social media campaigns.
- Internet of things (IoT) data streams: In IoT applications, data ingestion involves the real-time collection of data from IoT devices, sensors, and machines. This data monitors and controls various systems in industries like manufacturing, healthcare, and energy.
- Log and event data collection: For effective log and event data collection, data ingestion includes gathering log files and event data from applications, servers, and network devices. This data is then used to monitor system health and performance.
- Real-time financial market data feeds: Data ingestion is crucial for real-time financial market data feeds. It involves collecting financial data from various sources such as stock exchanges, news feeds, and economic indicators. This data is then analyzed to identify market trends and make data-driven investment decisions.
Conclusion
So, what is data ingestion in big data? It is the process of acquiring and importing data, enabling its prompt utilization or storage within a database. As an integral part of modern data architectures, a well-designed data ingestion infrastructure facilitates the seamless collection of data with robust connectivity to handle both batch and real-time data efficiently.
Confiz provides comprehensive solutions for unified data ingestion, enabling enterprises to gather data from multiple sources in a single, streamlined process. So, contact us at marketing@confiz.com and let our data engineers design and implement efficient data ingestion pipelines tailored to meet your business’s specific needs.
