Businesses analyze data from multiple sources to gain insights into their customers, market and current trends. These insights drive decisions that can make or break a company’s future. With such high stakes, organizations need a method to gather all the data from applications, websites and third-party platforms and move it to a centralized storage system without reducing data integrity. Data ingestion is the process used to collect and move data into storage. It’s a crucial part of the data pipeline and must be designed in a way that enables data teams to get near-instant access to the latest data without any discrepancies.

In this article, we’ll explain what data ingestion is, how it works and the different types of ingestion processes. We’ll also cover the benefits and challenges of data ingestion, along with two common approaches to building an ingestion pipeline.

What is data ingestion? 

Data ingestion is the process of collecting data from one or more sources and loading it into a staging area or object store for further processing and analysis. Ingestion is the first step of analytics-related data pipelines, where data is collected, loaded and transformed for insights.

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There are three main elements of data ingestion:

• Sources: A source is any application or website that generates relevant data for your organization. Customer apps, software used for marketing, sales or CRM, internal databases and document stores are examples of sources.

• Destinations: A destination can be a centralized storage system like a cloud data warehouse or a data lake. It can also be an application, like a business intelligence tool or messaging system.

• Cloud migration: Data ingestion can move data from traditional storage into cloud-based storage and processing tools. This is necessary for modern businesses to break down data silos and handle ever-growing data loads.

While manual data ingestion might work for minor use cases with few data sets, organizations looking to thrive in a data-driven world need automated data ingestion. This ensures that data is collected without delay and analysts have fresh data to work with.

For example, Fivetran is a data integration solution that facilitates automated data ingestion using pre-built connectors, which can connect to sources in minutes and help construct a reliable data pipeline without any code.

Types of data ingestion 

There are three data ingestion methods: streaming, batch and hybrid. Here’s a closer look at each type.

Streaming

Streaming or real-time data ingestion uses mechanisms like change data capture (CDC) to move data from sources in real time. As soon as there is a change in source data, real-time ingestion systems sync these changes without interrupting the current database workload.

This data ingestion is vital for time-sensitive use cases where a business must instantly react to new information. Stock market trading is an example of a use case for streaming data ingestion. Streaming data ingestion also powers informed operational decisions via data pipelines that rapidly identify insights.

Batch

Batch data ingestion moves data in batches at scheduled intervals. This could be minutes, hours, days or weeks. Developers and data teams can use this data ingestion type to collect data based on schedules and trigger events. For example, analysts can use batch ingestion to collect specific data sets from a CRM platform on the same day every month. This type of data ingestion is used to collect data that doesn’t influence real-time decision-making or operations. It is also used to collect particular data points for deeper analysis periodically.

Hybrid

Hybrid data ingestion combines aspects of both real-time and batch ingestion. Two common hybrid methods are Lambda architecture-based and micro-batching. Lambda architecture-based data ingestion has speed, batch and serving layers. The last two layers ingest data in batches, while the speed layer instantly ingests data not synced by the other two. The aim is to make data available for querying with low latency. 

Micro-batching balances latency and throughput. A server executes a batch operation every few milliseconds or seconds. This method is ideal when users want to process data in batches but want it to be quicker than the standard batch ingestion. For example, telematics services can use micro-batching to get vehicle updates every 1–2 seconds.

Benefits of data ingestion

Data ingestion is important for teams to handle and analyze data efficiently.

There are six crucial advantages of data ingestion:

• Centralize data: Data ingestion systems collect scattered data from a variety of sources and load it onto a staging area, where analysts can easily apply transformations for processing and analysis. Centralized data provides context to data. Data teams can see how each data set serves the organizational goals and make decisions based on this.

• Increase data availability: Efficient data ingestion provides near-instant data access, meaning analysts get the latest data to work with. This is especially true when using ELT data pipelines. They can apply transformations to fresh data to get more relevant and actionable insights.

• Improve decision-making: Improved data access and availability helps leaders and analysts make informed decisions for the marketplace. For example, they can gain insights into a new customer trend and capitalize on it by tweaking their product or service offerings.

• Boost productivity: Automated data ingestion removes low-impact pipeline building and maintenance tasks from the workload of data engineers and developers. This allows them to work on more critical tasks and innovation. 

• Enhance user experience: Companies can use the latest data to serve their customers better. Data-driven insights enable them to create better apps and tools for customers or identify and solve user problems quickly. Optimizing user experience is vital for customer loyalty and business growth.

• Simplify data collection: Automated data ingestion, powered by platforms like Fivetran, simplifies data collection. On the platform, it’s easy to set up and edit connectors to gather data from any source. This eliminates the need to code and implement the integrations between data sources and destinations manually.

Common data ingestion challenges

While data ingestion helps businesses, implementing the correct processes can be challenging.

Data teams and engineers face these common challenges when creating an efficient data ingestion system.

Complex data needs

Data ingestion is simpler in the initial stages of a company’s growth since there are a limited number of sources and data types. However, building a data ingestion process becomes increasingly complex as the business grows and the volume and variety of data types increases. 

Manual data ingestion systems become nearly impossible to build and maintain at this level, with engineers being forced to constantly build integrations for new sources, modify existing systems and tackle any errors in data replication, pipeline failures and more. These tasks bog them down, leaving no room for innovation or feature updates.

This is why most modern companies are turning to fully-managed and automated data pipeline solutions like Fivetran. Solutions like these are simpler, more affordable, easier to scale and less labor-intensive than spending lengthy periods manually coding a data pipeline.

Cyber-security challenges

When data is moved from source to destination, it must be protected from unauthorized access at all costs. This can become tiresome when manually implementing security features, especially if your engineers are already overloaded with building and maintaining pipelines.

Maintaining security during data ingestion becomes easier with an advanced data integration solution. Fivetran has dynamic in-built protection from the start, including automated column hashing, data encryption during transit and rest, detailed logging, user permissions and data purging.

Data integrity concerns

If data is corrupted during the ingestion process, analysts will make decisions based on incorrect information. This could have significant negative consequences. Your data ingestion pipeline must copy the data exactly as it is and prevent errors using concepts like idempotence.

Idempotence is a crucial data pipeline feature that ensures there is no data duplication or erroneous records when sync failures occur. That is, if an error causes the source to go offline mid-sync, then idempotent pipelines will sync correctly when the sync resumes.

Other data integrity and governance policies must also be implemented to maintain the reliability and accuracy of data. 

Compliance issues

Data teams must comply with an increasing number of regulatory standards and data privacy laws. Understanding and building an ingestion process to be legally sound can be time-consuming and frustrating.

It is better to use an already compliant third-party data ingestion solution like Fivetran, which has a comprehensive privacy, security and compliance program. Fivetran has SOC 2, ISO 27001 and PCI DSS Level 1 certified. It also complies with E.U. & U.S. laws with GDPR and CCPA.

Data ingestion and ELT

Extract, Load, Transform (ELT) is a data integration method focused on faster data availability, flexibility and scaling. It is preferable to Extract, Transform, Load (ETL), an older method with limited flexibility and lesser fault tolerance.

In an ELT data pipeline, data is collected from sources (called extraction), loaded into storage or a target data system and then transformed as needed. This is better than ETL, where data is first transformed before being loaded into storage.

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Since an ELT data pipeline decouples the extraction and transformation processes, data ingestion is much quicker. There is also no need to include complex transformations as part of the pipeline. This allows analysts and data scientists to get faster access to the data. They can then transform this data and use business intelligence tools to gain insights. ELT pipelines on cloud platforms like Fivetran allow effortless scaling. Teams pay for any additional resources they need and get instant access to them.

ELT data integration also gives analysts more control over what they do with the data. It enables analysts and engineers to apply pre-built transformations or custom transformations to manipulate data as needed. An ELT pipeline can also be installed in minutes using Fivetran to connect data sources and destinations, such as a cloud data warehouse, data lake or business intelligence tools.

Data ingestion approaches

There are two main approaches to data ingestion: you can either manually code a data pipeline yourself or use a data integration platform to streamline this process.

Manual coding

This is the hard way. Engineers and developers manually write each line of code required to build a data pipeline from scratch. As you can imagine, this is extremely time-consuming, as building integrations with each app requires hundreds of lines of code. They must also write code for every new source added and accommodate varying data types. Then, they must write code to fix any errors or pipeline failures. All this work is also labor-intensive, meaning companies spend a significant amount of money hiring engineers to set up, monitor and fix pipelines.

Data integration platforms

A much more pain-free method is to use fully managed data integration platforms with features to help you build a reliable data pipeline in minutes. Buying the resources you need for data ingestion and integration is much faster and more affordable than the countless hours spent doing it manually.

Fivetran has pre-built connectors and transformations to streamline your data pipeline further. The platform can replicate data from SaaS applications and large databases without impacting your data workflow. Moreover, it’s fully managed, so you don’t have to worry about updates or maintenance.

Conclusion

Data ingestion is the backbone of analytics-related data pipelines. Ensuring fast and efficient ingestion is vital for data teams gaining access to the latest data. They can drive business growth via informed decisions and insights. To fully capitalize on the potential of data integration and analysis, it is best to use a dynamic data integration solution that lets your data team focus on innovation and insights rather than mundane data pipeline tasks. Sign up for a free trial of Fivetran and discover how our platform can elevate your data pipelines.