In the modern era, businesses are undergoing a significant transformation in which business operations are becoming increasingly data-intensive. Companies gather data from various sources, including applications, SaaS solutions, social channels, mobile devices, IoT devices, and others.
In order to make the best use of gathered data for making productive decisions, businesses must pull such data from all available sources and consolidate it in one destination for optimal analytics and data management.
Data Ingestion is a major data handling approach that transfers data from one or more external data sources into an application data store or specialized storage repository.
In this article, you will learn about Data Ingestion. You will also explore the various Data Ingestion types, best practices, frameworks, and parameters.
Table of Contents
- What is Data Ingestion?
- Data Ingestion Types
- Data Ingestion Best Practices
- Data Ingestion Frameworks
- Parameters of Data Ingestion
A fundamental understanding of the data handling process.
What is Data Ingestion?
Data Ingestion is the process of ingesting massive amounts of data into the organization’s system or database from various external sources in order to run analytics and other business operations.
To put it another way, Data Ingestion is the transfer of data from one or more sources to a destination for further processing and analysis. Such data comes from a variety of sources, such as IoT devices, on-premises databases, and SaaS apps, and it can end up in centralized storage repositories like Data Lakes.
Refer to What is Data Ingestion? 10 Critical Aspects guide, to learn more about Data Ingestion and its architecture.
Data Ingestion Types
Depending on the business requirements and IT infrastructure, various Data Ingestion Types were developed such as real-time, batches, or a combination of both. Some of the Data Ingestion Types are:
1) Real-Time Data Ingestion
The process of gathering and transmitting data from source systems in real-time solutions such as Change Data Capture (CDC) is known as Real-Time Data Ingestion. This is one of the widely used Data Ingestion Types used especially in streaming services.
CDC continuously monitors transactions as well as redo logs and moves changed data without trying to interfere with database workload. Real-time ingestion is critical for time-sensitive use cases such as stock market trading or power grid tracking, where organizations must react quickly to new data.
Real-time Data Pipelines are also necessary for quickly making operational choices and defining and acting on new insights. In real-time data ingestion, as soon as data is generated, it is extracted, processed, and stored for real-time decision-making. For example, data obtained from a power grid must be continuously monitored to ensure power availability.
2) Batch-Based Data Ingestion
The process of collecting and transferring in batches at regular intervals is known as Batch-based Data Ingestion. When data is ingested in batches, it is moved at regularly scheduled intervals, which is highly advantageous for repeatable processes.
With Batch-based Data Ingestion types, data can be collected by the ingestion layer based on simple schedules, trigger events, and any other logical ordering. When a company needs to collect specific data points on a daily basis or simply does not require data for real-time decision-making, batch-based ingestion is beneficial.
3) Lambda-Architecture-Based Data Ingestion
The Lambda architecture is one of the Data Ingestion Types. Its configuration includes both Real-Time and Batch ingestion methodologies. The Lambda architecture balances the benefits of the two methods mentioned above by utilizing batch processing to provide broad views of batch data.
Furthermore, it employs real-time processing to provide viewpoints of time-sensitive data. The configuration includes batch, serving, and speed layers. The first two layers index data in batches, while the speed layer indexes data that has yet to be picked up by the slower batch and serving layers in real-time. This continuous hand-off between layers ensures that data is available for querying with minimal latency.
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Data Ingestion Best Practices
To have a seamless Data Ingestion workflow, you can follow some of the best practices for Data Ingestion below:
- Data Ingestion via Self-service
- Process Automation
- Using Artificial Intelligence
- Frequent Data Governance
1) Data Ingestion via Self Service
Many organizations have multiple data sources from which they fetch data for making data-driven decisions. Data continues to grow in size and metrics, necessitating the addition of resources to organizations to handle the data.
If the ingestion process is self-service, the pressure to constantly add resources is relieved through measures such as automation, and the focus is now shifted to processing and analysis. The ingestion process becomes extremely simple, requiring little to no intervention from technical personnel.
2) Process Automation
Manual data handling and processing techniques can no longer be relied on as organizational data grows in volume and complexity. Therefore, automating the entire process saves time, reduces manual interventions, decreases system downtimes, and boosts productivity.
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Other benefits of automating the ingestion process include architectural consistency, collaborative management, error management, and safety. These benefits help reduce the amount of time it takes to process data.
3) Using Artificial Intelligence
In the Data Ingestion process, AI principles such as statistics and machine learning algorithms minimize the need for manual involvement. The number and frequency of errors increase when manual intervention is used in the data ingestion process.
Using Artificial Intelligence not only eliminates these errors but also speeds up the process and increases accuracy levels. A number of products have been developed that use machine learning and statistical algorithms to automatically infer information about the data being ingested, thereby reducing the need for manual labor. Open-source systems such as Data Tamer and commercial products such as Tamr, Trifacta, and Paxata are some examples.
4) Frequent Data Governance
After you go through the process of cleaning up your data, you’ll need data governance. This entails initiating data governance, with a data steward in charge of ensuring the quality of each data source. This includes defining the schema and cleansing rules, determining which data should be ingested into which data sources, and managing the treatment of dirty data.
Data Governance encompasses more than just data quality; it also includes data safety and compliance with regulatory norms such as GDPR and master data management. Achieving all these goals necessitates a cultural shift in how the organization views data, as well as a data steward who can champion the necessary efforts and be held accountable for the results.
Want to explore more about Data Governance Tools? Refer to 10 Best Data Governance Tools to learn more.
Data Ingestion Frameworks
Now that you have explored the various Data Ingestion Types, let’s discover some of the robust Data Ingestion Frameworks to load your data from various sources:
1) Apache Flume
Apache Flume is a distributed, reliable, and data collection, aggregation, and transfer service. It has a simple and adaptable architecture based on streaming data flows.
Apache Flume is fault-tolerant and robust, with tunable reliability mechanisms and numerous failover and recovery mechanisms. It also employs a straightforward, extensible Big Data Security model that enables an online analytic application and data ingestion process flow.
2) Apache Nifi
Apache Nifi is yet another excellent data ingestion tool, providing an easy-to-use, powerful, and dependable system for data processing and distribution. It supports both robust and scalable directed graphs of data routing, transformation, and system mediation logic.
Apache NiFi has a high throughput, low latency, loss tolerance, and guaranteed delivery. The data intake engine makes advantage of its schema-less processing technology. This means that each NiFi processor is responsible for interpreting the data sent to it. Furthermore, Apache NiFi is designed to work as a standalone tool or as part of a cluster using its own in-built clustering framework.
Wavefront is a high-performance, cloud-hosted streaming analytics service for ingesting, storing, visualizing, and monitoring all types of metric data. The Wavefront platform has the ability to scale to very high query loads and data ingestion rates, reaching millions of data points per second.
Wavefront enables users to collect data from over 200 different sources and services, such as DevOps tools, cloud service providers, big data services, and others. In addition, Wavefront users can view data in custom dashboards, receive alerts on problem values, and perform functions like anomaly detection and forecasting.
4) Precisely Connect
Precisely Connect (formerly Syncsort) offers a data integration solution for high-end operations like advanced analytics, data migration, and machine learning via real-time or batch ingestion.
Users can use the Precisely Connect platform to access complex enterprise data from various sources and destinations for ELT and CDC purposes. Its sources and destinations include mainframe data, Relational Database Management Systems (RDBMS), data warehouses, big data services, data lakes, streaming platforms, and others.
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- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Data Ingestion Parameters
Data Ingestion is the most difficult and time-consuming part of the entire data processing architecture. The following are the essential parameters to consider while creating a Data Ingestion solution or pipeline:
1) Data Velocity
The data velocity parameter determines the speed at which data flows in various sources such as machines, networks, media sites, and social media.
2) Data Size
This parameter is concerned with the amount of data that is generated from various sources and ingested into the pipelines. Massive amounts of data from sources may be required for ingestion into the data pipeline, which increases the time required.
3) Data Frequency
The rate at which data is processed is defined as data frequency. Data can be processed in either real-time or batch mode. In real-time, data is moved instantly, while in batch processing, data is first stored in batches and then moved into the pipelines.
4) Data Format
Data can be in a variety of formats, including structured, semi-structured, and unstructured data. Similarly, data can be ingested in a variety of formats into the pipeline.
Data can be structured, such as tabular data; unstructured, such as photos, audios, and videos; or semi-structured, such as JSON files and CSS files. This parameter governs the formatting options available during the data ingestion process.
In this article, you learned about Data Ingestion. You understood more about Data Ingestion types, best practices, frameworks, and parameters. You explored the Real-Time, Batch-based, and Lambda-based Data Ingestion Types.
This article only focused on a few attributes of best practices and frameworks. However, you can later explore other Data Ingestion best practices like network bandwidth, scalability maintenance, and data compression.
To stay competitive, most businesses now employ a range of automatic Data Ingestion solutions. This is where a simple solution like Hevo might come in handy!
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