A Practical Guide to Data Ingestion
Here is your essential guide to data ingestion: what it is, the process, use cases, and challenges. We've also included the top considerations for selecting the right solution and much more.
What is data ingestion?
Data ingestion is the process of importing data from different sources to one storage medium, such as a database, data warehouse, data mart, or document store. This allows a business or organization to access the data, use it, and analyze it.
There are three main ways to ingest data. The method an organization chooses will depend on their data sources and how quickly they need to access the data after ingestion.
In batches. Data is imported in batches at set periods of time, then all processed at once. It can be grouped according to set criteria if specified. Batch processing is suitable for data that doesn’t need to be updated in real time.
In real time. Data is imported as soon as the ingestion layer recognizes it, but it isn’t grouped together. This is also known as "stream processing." It’s best suited to time-sensitive data that needs to be analyzed quickly so decisions can be made swiftly.
In micro batches. Data is divided into groups and ingested in small increments, making the updates almost as fast as real-time ingestion, without requiring the same level of resources.
Regardless of the method used, data files must be validated and sent to the right place in order for ingestion to be most effective. Organizations need a clear, complete picture of their data so they can make informed decisions and avoid coming to erroneous conclusions. Indeed, a survey by Gartner revealed that 58% of businesses see data and analytics alignment with business strategy as one of the top three drivers of success.
The data ingestion process
Data discovery
The initial stage of the data ingestion process allows organizations to answer some questions in order to get a better understanding of the data available and the potential it has to assist your business aims:
What data is available?
Where is it sourced from?
How would your organization use it?
How would using the data be beneficial?
How can you access it?
Data acquisition
During the data-acquisition phase, the data is collected from its sources. There can be many sources and formats, while the data itself can be large in volume.
Data Validation
The data is checked for accuracy and consistency before it’s used by the organization, so any business decisions made as a result are based on accurate analytics.
Data Transformation
The data is converted into a uniform format so it’s ready to be analyzed.
Data Loading
The data is loaded into the organization’s chosen storage medium. This is the final step before analysis.
Gain control of your data
Types of data ingestion tools
You’ll need to decide what kind of data ingestion tool is right for your organization. A good tool can extract various types of data from a variety of sources and process it, while showing you what stage the data is at in the system. It will also include security and privacy features to protect to protect the data.
Different types of data ingestion tool include:
Where the code required for your data pipeline is written manually and from scratch. This gives you more control but hand-coding can take up a lot of time if and when the code needs to be rewritten.
Where you can create your data pipelines using a drag-and-drop interface, instead of relying on coding. This can be simpler initially, but it may become more difficult to manage if you need a lot of data pipelines.
Which have features for every step of the data ingestion process. This gives you a clear overview but requires specialists for each area, which means change can happen slowly.
A comprehensive method and toolset for automating common data management, engineering, and operations tasks. Like DevOps, it can help formalize data management practices in a systematic, agile, and mostly-automated way.
Why is data ingestion important?
Data ingestion allows employees across an organization to access the data they need. Once it has been through the data-ingestion process, it’s presented in a consistent format, so it can be fully understood and aid decision-making. This can save time, especially when dealing with large quantities of data from different sources.
Data ingestion challenges
Data security
Centralizing data can potentially increase security risks, which is why it needs to be addressed. If data sources are compromised they can poison data fed to the ingestion process, corrupting the information later used for analysis.
Data scale and variety
Some organizations will use data ingestion for a large quantity of different types of data, which can make it harder to ensure data quality.
Data fragmentation
Different areas of the organization may use the same data from the same sources. This can lead to duplication during the ingestion process. Duplication can lead to errors and results in more resources being used.
Data quality
High-quality data ensures the resulting analytics are accurate and can be used by the organization to make the best business decisions.
Cost
Larger amounts of data equate to higher costs for storage systems and servers. Organizations must also take care to comply with regulations, which adds to costs as well.
Manual approaches and hand-coding
With the sheer quantity of data available now, manually managing it can take up a lot of time and resources.
Addressing schema drift
Schema drift occurs when the schema changes in the source database. This must be addressed quickly, otherwise data engineers will spend time rewriting code and users will be unable to access the data they need.
Specific data models or schema
Some data-ingestion solutions require ETL (extract, transform and load) to fit a specific data model or schema, which results in lengthy projects and a reluctance to use all existing data. This results in missed opportunities for insights and subsequent actions.
Data capture
It takes time and effort to capture data from different sources and write the necessary code. Out-of-the-box connectivity to data sources and targets speeds up the process.
Real-time monitoring and lifecycle management
It’s difficult, if not impossible, to monitor the data-ingestion process, identify any errors, and make the changes needed if they arise. Making automation part of the process ensures any anomalies are spotted and rectified shortly after they arise.
Data ingestion use cases
Batch processing
Batch processing is best suited to applications that do not need an immediate response. Common examples include:
Data analysis, for identifying trends and making better business decisions
Data back-up and recovery, for protecting against data loss or corruption
Data consolidation, for combining data from multiple sources into a single storage system
Data mining, for identifying insights and new opportunities
Real-time processing
Real-time processing is best suited to organizations that need to respond immediately. Having access to the most up-to-date information drives quick decision-making in situations like:
Detecting fraudulent and unauthorized activity, and identifying patterns in these cases
IoT (Internet of Things) data processing, prompting organizations to maintain and optimize their systems
Micro-batching
Micro-batching is best suited when you need data quickly, but not in real-time. Situations include:
Analyzing user behavior on your website and monitoring how changes affect the way people use it
Processing orders on e-commerce websites
Processing and sending invoices on billing systems
Looking up data stored in a large database to combine all queries instead of running them separately
Top considerations for selecting the right solution
When looking for a data-ingestion solution, look out for the following key capabilities:
Data extraction
Data processing
Scalability
Security and privacy features
Data flow tracking and visualization
Unified experience of data ingestion
Ability to handle unstructured data and schema drift
Versatile, out-of-the-box connectivity
High performance
Wizard-based data ingestion
Real-time data ingestion
Cost-efficiency
Data ingestion, ETL and ELT
A common part of data ingestion is ETL or ELT. ETL stands for "extract, transform, and load," and refers to the process of preparing data for long-term storage in a data warehouse (a repository where data that’s been treated is stored) or data lake (a repository where large volumes of data can be stored in their original form). Raw data is extracted, transformed, then loaded into the data warehouse or data lake. The raw data is not available once the transformation has taken place.
Different styles of data integration can streamline the process and may cause a shift from ETL to ELT. ELT stands for "extract, load and transform," because the transformation step can be used only when necessary, leading to quicker results. The raw data is extracted and loaded into a data warehouse or data lake, then transformed when the information is required, with the raw data still available until this transformation takes place.
ETL is still useful as an independent tool that integrates data between multiple sources and targets, each of which has a specific data format requirement. ELT is more suitable for a single target that controls the ELT component.
How is data ingestion different from data integration?
The data-ingestion process involves importing data files from different sources to one storage medium. The data-integration process involves combining data files from different sources into one format.
Data ingestion with Quantexa
Loading a customer’s data into the Quantexa platform using data ingestion enables customers to connect multiple data sources for use in entity resolution and graph analytics. Data ingestion consists of ETL and data modeling to prepare the data, including out-of-the-box solutions for data cleansing (the standardization of input strings and removal of unnecessary data) and parsing (the extraction of multiple elements from a single input string). It can be deployed in batch or real-time modes.
Raw data is mapped to a hierarchical model, structuring data in a way that enables the expression of relationships between different entities. Quantexa can ingest data from any data model, with no prior transformation required, thanks to schemaless data ingestion. Our low-code-data-ingestion framework is a collection of configuration files accessed through Quantexa’s developer interface. The files can be customized by the customer. It provides a streamlined, standardized process of getting data into the system and allows users to perform complex tasks without coding.
Quantexa’s data-ingestion process also includes a no-code-data-ingestion UI, a point-and-click interface that reduces reliance on technical expertise, allowing users without coding experience to load data onto the platform.
Why Quantexa?
We offer a standardized framework for ingesting data.
Data sources can be added through a UI without any code.
There’s no fixed data model or schema, so you can connect all your data in its original familiar form.
Rapid data ingestion and preparation saves your organization time during a complex process thanks to powerful, out-of-the-box parsing and cleansing models.
The system provides flexibility, enables analytics, and improves data quality.
Useful links
We’ve discussed a lot in this guide, but there might still be more you want to discover about data ingestion. Browse these handy sources to learn more.