Data ingestion is the complex process of collecting data from multiple sources into a single destination for further processing and analysis. Ensuring that high volumes of data can be ingested quickly, easily, and frequently is the first step in the data management process. In this article, we’ll cover why data ingestion is necessary, the types, and the difference between data ingestion and ETL or ELT processes. Finally, we’ll dive into the challenges and the benefits of getting it right to guarantee data teams are able to extract the value downstream.
Why Is Data Ingestion Necessary?
Today, data drives considerable parts of our lives, from crowdsourced recommendations to AI systems identifying fraudulent banking transactions. The same is true for businesses. IDC, a market intelligence firm, predicts the volume of data created each year will top 160 ZB by 2025.
Source: Data Age 2025 Report
The more data businesses have available, the more robust their potential for competitive analysis becomes. Organizations need access to all their data to draw valuable insights and make the most informed decisions about business needs. An incomplete picture of their available data can result in misleading reports, incorrect analytic conclusions, and blind decision-making.
In order to take advantage of the incredible amount and variety of data available, the data needs to be ingested into a data platform where it can be further processed. Without high-quality ingestion, driving insights from raw data is virtually impossible.
Therefore, data ingestion is necessary because it takes organizations from data asymmetry to data symmetry: from disparate data located in different data stores to harmonized data, typically in a singular, limited number of data stores. Enabling data ingestion is essential to moving to a standardized environment where data teams can work with larger aggregated data sets and drive business value downstream.
Types of Data Ingestion
There are three common types of data ingestion. Deciding which type is appropriate will depend on the kind of data you need to ingest and the frequency at which you need it.
Batch-Based Data Ingestion
This is the process of collecting and transferring data in batches. Batches can be small or large and are assembled at scheduled intervals. For the most part, batch-based ingestion is used when an organization needs very specific data regularly. Batch is the most common form of ingestion.
Real-Time Data Ingestion
This is the process of transferring and collecting data in real-time using streaming technology. Solutions like this allow constant monitoring of data changes without scheduling the workload. This type of data ingestion is used when data sources continually produce data and businesses require extremely low latency analytics.
Serverless Architecture-Based Data Ingestion
This solution usually consists of custom software that uses both real-time and batch methods. This is the most complicated form of data ingestion that requires multiple layers of software that manage parts of the ingestion. There is a consistent hand-off between the many layers to ensure data is readily available for review.
It’s important when doing data ingestion to take the approach that optimizes each system’s strengths. For example, queue-based or streaming systems are a requirement for real-time data. However, queues can quickly hit the limits of what they’re designed for if the level of transformation complexity starts to rise beyond what basic tools were built to do. A better option, in this instance, might be a data orchestration or other data management tool. When it comes to data ingestion, it’s vital to apply the right tool for the right use case.
Data Ingestion vs ETL and ELT Processes
Extract, transform, and load (ETL) refers to the process by which teams have traditionally loaded databases: extract data from its source, transform it, and load it into tables to be accessed by consumers. For most businesses, the level of transformation required to simply load data is not enough.
Traditional ETL tools could not keep up with the necessary levels of transformation complexity, so the industry evolved to ELT: extract, load, and transform data. In ELT, the transformation step is moved to the end to remove the need to transform all source data before loading. This adds flexibility to configure long-running transformations and use a wider range of transformation tools.
Source: Nicholas Leong
While data ingestion is often understood as the Extract and Load part of ETL and ELT, ingestion is a broader process. Most ETL and ELT processes are focused on transforming data into well-defined structures optimized for analytics. The focus of data ingestion is gathering data and loading it into a queryable format, with relevant metadata, to prepare it for further downstream transformation and delivery. Ingestion enhances ‘extract and load’ with metadata discovery, automation, and partition management.
Data Ingestion Challenges
The growth of the data available, the increase in diversity and complexity of data, the explosion of data sources, and the different types of data ingestion, quickly increase the intricacies of the data ingestion process.
Data volume has exploded, and the data ecosystem is growing more diverse. Data can come from countless sources, from SaaS platforms to databases to mobile devices. The constantly evolving landscape makes it difficult to define an all-encompassing and future-proof data ingestion process. This has created opportunities for ingest-centric vendors to monetize this pain point with single-purpose tools that create gaps in the data management supply chain. Coding and maintaining a DIY approach to data ingestion becomes costly and time-consuming.
Data volume has exploded, and the data ecosystem is growing more diverse. Data can come from countless sources, from SaaS platforms to databases to mobile devices. The constantly evolving landscape makes it difficult to define an all-encompassing and future-proof data ingestion process. This has created opportunities for ingest-centric vendors to monetize this pain point with single-purpose tools that create gaps in the data management supply chain. Coding and maintaining a DIY approach to data ingestion becomes costly and time-consuming. You can reduce the complexity with free data ingestion to remove friction and allow data teams to accelerate business value creation.
Legal and compliance requirements add a challenging layer to the data ingestion process. When transferring and consolidating data from one place to another, there is a risk of security. For example, healthcare data in the United States is affected by the Health Insurance Portability and Accountability Act (HIPAA), organizations in Europe need to comply with the General Data Protection Regulation (GDPR), and companies using third-party IT services need auditing procedures like Service Organization Control 2 (SOC 2). A holistic approach and planning to minimize the impact of these requirements is essential to guarantee the initial ingestion of data is adequate and that the data management process won’t suffer downstream.
Out-of-the-box connectors replace the need for coding data ingestion processes. Most data ingestion vendors provide common connectors. However, can they cover the complexity organizations are dealing with today? For databases, can the connectors support Change Data Capture (CDC) streams or multiple replication strategies? Do connectors support intrinsic and configurable metadata to inform downstream systems? Opting for an option that offers connector flexibility is paramount to make sure data teams don’t get stuck and are able to tap hard-to-reach data.
Four Tips to Get Started
While the data ingestion process can get complicated quickly, there are proven approaches that can alleviate common pains down the road.
Determine the Level of Data Volatility
Volatile data that is constantly changing will require different ingestion patterns than immutable data. Understanding how the data changes over time is an important consideration when setting up a new dataflow. Using the right connector for the scenario at hand will help optimize the system and keep complexity at bay.
Hold Onto More Than You Need
Having to go back and historically backfill data can be challenging due to the rate limits, granularity of data, or inability to access old data. Decreasing storage costs allows for storing more than is necessary so that it is present for recomputation. Keep the data you got previously, even if it needs to be enriched later. It’s never fun when roadmaps hit speed bumps on needing a complex historical data pull.
Understand the Business Value
Ask yourself if you have the code to connect to the external system and be sure you can test and validate connections. Start with the basics: can you look up the entity on DNS? Can you create a TCP connection to log in and authenticate to the system? These checks are essential in an increasingly cloud-based world and can save later debugging time.
Slow and Steady Wins the Race
Write the data and make sure it’s getting properly ingested. Preserve the structure of the data and check that you aren’t dropping columns or records. Map and match the data from the originating system to the new system. Always keep your timestamps, data formats, business logic—everything that makes your system tick—intact as you move upstream data in. Go lightweight at first and bring in simple data, view it, audit it, and transform it later. After, you can start looking at how to make things faster and more efficient.
Final Thoughts About Data Ingestion
Data ingestion enables organizations to load all their data from various data sources into a single destination. While data ingestion is the first step in the data management process, it’s crucial to make data available for further processing and analysis. Although this is not a simple process and can be costly and time-consuming, a well-thought-out data ingestion strategy can directly influence organizations’ decision-making.