Data ingestion is the process of pulling and collecting raw data from various sources to a destination where it can be accessed, processed, or analyzed further.
The word “ingestion” implies taking in something. In this case, it is data copied to another location. The data may undergo minimal to no transformation before it reaches its destination. Destinations can be databases, data lakes, data marts, data warehouses, and more. It can be in the cloud or on-premises.
Data ingestion is the first step before other data processing schemes proceed. These next steps include but are not limited to data integration, machine learning, and artificial intelligence. Check out the image below:
While the above illustration looks simple, the actual implementation may not. It depends on the requirements. Data ingestion gone wrong will result in the following:
- Wasted compute resources
- Delayed projects and lost revenue
- Downtime
- Compliance fines
Learn the importance of data ingestion, its types, myths, challenges, best practices, and more. information in this article will help you to mitigate the negative results described above.
Let’s begin.
Data Ingestion vs. ETL
At first glance, the data ingestion process looks like ETL (Extract, Transform, Load). This makes it confusing for beginners to grasp different data management techniques.
Table of Differences Between Data Ingestion and ETL
The following is a rundown of the differences in table format:
| Aspect | ETL (Extract, Transform, Load) | Data Ingestion |
|---|---|---|
| Purpose | Process of extracting, transforming, and loading data into a target system or database. | Process of collecting raw data from various sources to a destination for further processing. |
| Scope and Complexity | Comprehensive data processing including transformation, cleansing, and enrichment. | Initial phase of data processing, focusing on pulling and collecting data. |
| Transformation | Heavy transformation involving complex operations like joins, aggregations, and calculations. | Minimal or no transformation. |
| Target System | Typically, data warehouses, data marts, analytical databases, or another system. | Data lakes, databases, staging areas, or other processing environments. |
| Timing | Can be batched or real-time. But typically, batch-oriented with scheduled data processing jobs. | Can be batch-oriented or real-time, depending on the use case and requirements. |
| Result/Output Form | Processed data | Raw data |
| Tools | Informatica, Talend, Microsoft SSIS, Skyvia | Apache Kafka, AWS Glue, Skyvia, Azure Data Factory |
The above highlights the main differences:
- Transformation: ETL is heavy in transformation while data ingestion involves minimal to zero transformation. Data type conversion from number to character strings is an example of minimal transformation. Examples of complex transformations in ETL are pivoting and aggregating.
- Process: Data ingestion can be a straightforward copy, but ETL handles more complex scenarios.
- Output: ETL results to processed data that is different from its sources. Data ingestion results to raw data, even if minimal transformations are applied.
In certain use cases, data ingestion and ETL can happen in succession.
Data Ingestion and ETL Can Overlap
In cases where there are various data sources, ingestion happens first. It collects data from various sources to produce the raw data needed by ETL. All collected raw data goes to a staging area or raw zone. Then, ETL proceeds by using the staging area for the extraction (The “E” in ETL). In other words, data ingestion prepares the data for ETL.
The above scenario is illustrated below. The last step of data ingestion becomes the first step of ETL.
The intersection between data ingestion and ETL is in the staging area. This staging area can be an on-premises or cloud database, like SQL Server or AWS RDS. This technique also makes it easier for ETL to proceed, as it does not need to deal with different locations.
Why is Data Ingestion Important?
Data ingestion is a crucial first step in various data processing efforts. The following are some key reasons why this is important:
Data Integration
As mentioned earlier, ingesting data from various sources makes it easier for ETL to proceed. Other data integration methods also benefit from a source with a single data format. Data replication and ELT (Extract, Load, Transform) are some of them. The result is a backup or a unified source of truth for analysis and decision-making. This will also lead to better business outcomes and competitive advantage.
Data Accessibility
Data Quality Improvements
Organizations can perform data quality checks after ingestion is done. They can remove duplicates and other data cleansing methods to ensure a reliable data asset. By doing this early, organizations can ensure correct analysis and reliable reports.
Real-Time Insights
With real-time data ingestion, organizations can analyze data as it comes. They can identify trends, patterns, and opportunities quickly. With this, fraud detection, IoT analytics, and other critical monitoring systems will function as expected.
Scalability and Flexibility
Today’s data landscape evolves rapidly. Gigabytes of data can become petabytes in time. An effective data ingestion strategy can adapt to changes in schema, volume, and velocity of data. Ingesting large volumes of data opens the way to analyze trends, customers, and operations. Using scalable architectures and approaches allows for processing data at scale. With that, it can accommodate growing volumes of data and evolving business needs.
Regulatory Compliance
Compliance fines are bad news for businesses. To avoid these, ingestion should use data governance and compliance measures early on. This will safeguard sensitive data and protect against regulatory penalties.
What are the Types of Data Ingestion?
Organizations can choose from data ingestion types based on their requirements. Factors affecting them depend on data volume, velocity, latency, and use case.
Batch Data Ingestion
Batch data ingestion involves collecting and processing data in batches or chunks. Ingestion may happen hourly, daily, weekly, or any preferred interval. This makes sense in scenarios where real-time processing is overkill to produce analysis and reports.
Streaming Data Ingestion
This type involves processing data as it is generated or received. It enables real-time analytics, monitoring, and decision-making. Streaming ingestion makes sense in time-sensitive applications. Examples of these are fraud detection, IoT analytics, and event-driven architectures.
Micro Batching
This approach ingests data in tiny or micro chunks rapidly. It does not process one row immediately like in Streaming Data Ingestion. But processing happens as soon as the micro chunks are available. This allows organizations to process data with limited resources for Streaming Data Ingestion. But allows faster performance than Batch Data Ingestion offers. Data ingestion is also classified based on the types of data to ingest. The following are some of them:
File-Based Ingestion
This type involves data stored in flat files like CSV, JSON, XML, or Parquet. Copies of data rows from these files move to another location like a database. Processing happens in batches as the file becomes available.
Log-Based Ingestion
Servers, applications, and devices generate logs of system events. These logs are available for ingestion to analyze errors, warnings, and other system information. This is good for operational analytics, troubleshooting, security monitoring, and compliance auditing.
API-Based Ingestion
This involves pulling data through APIs of cloud apps and services. This can happen in streaming or batch depending on what is possible with the cloud provider. The method can be REST or SOAP but internet connectivity is a must. Collecting data of this type makes sense in SaaS platforms like Salesforce.
Four Data Ingestion Myths
Data ingestion can quickly go wrong because of misplaced beliefs about it. Here are some of the myths and their corresponding realities:
1. “Data Ingestion is So Simple”
This is the belief that data ingestion is a simple process like moving a folder of files to another place.
Reality: The challenge levels up when the volume becomes bigger. Scalability and performance issues can occur. Another common problem is data quality issues. Data can be incomplete or with duplicates. The schema also changes over time. It can cause errors in the ingestion process. Depending on data integration requirements, ingestion can also become complex. These need careful planning, expertise, and active maintenance.
2. “All Data Needs to be Ingested”
This is a misconception to ingest all available data for any purpose.
Reality: Not all data is relevant for analysis. Ingesting all data can impact scalability, performance, storage, and costs. Effective data management involves prioritizing which data is relevant to the current requirements. Ingested data may also increase cloud cost on the number of rows processed, the storage size, and compute hours. So, ingest only the needed data.
3. “Data Ingestion is a One-Time Activity”
This a misconception that data ingestion is like fire and forget – a one-time activity. Once it is done, the process is complete.
Reality: The ingestion tool may allow a graphical process designer and automated runtimes. The fire and forget part means the administrator does not intervene every time data processing starts. Still, ingesting data needs active monitoring, maintenance, and optimization. Because data quality issues may happen over time and schemas also change. So, it needs ongoing attention and management.
4. “Technology Will Handle Everything”
The belief that technology will fix all data ingestion challenges.
Reality: Technology plays a good part in ingesting data. But that’s not the only focus. In fact, even having the best and latest can still fail. People, processes, and governance also matter. A successful data ingestion needs collaborating teams to align business objectives to it. Objectives and needs change over time. It also needs data governance and compliance with regulatory requirements. Laws surrounding data privacy and security also change over time. So, technology is not a do-all, fix-all.
What are the Challenges of Data Ingestion?
Data ingestion can go wrong and lead to unwanted consequences. The following are some of them:
Data Quality Issues
Incorrect analysis and wrong decisions feed on incomplete, inconsistent, or inaccurate data. Poor data quality comes from duplicate data and unvalidated data during user input.
Consequences: Missed opportunities, incorrect insights, and reputational damage.
Data Security Risks
Data breaches, leaks, and unauthorized access can happen with a lack of security measures during data ingestion.
Consequences: Regulatory fines, legal liabilities, and financial losses.
Performance Bottlenecks
Inefficient ingestion methods will make the entire data processing effort finish late. One example is deleting all data in the target and reinserting them from the source. It will have performance issues when data grows bigger. Other factors that will impact performance are slow network latency and resource contention.
Consequences: Delays in decision-making, reduced productivity, and increased infrastructure costs.
Scalability Challenges
Ingestion processes may struggle to scale with increasing data volumes. The top contributors are inefficient data processing algorithms and architectural constraints. At times, the reason is limited hardware resources.
Consequences: Missed opportunities for data-driven insights, decreased agility, and increased operational costs.
Data Governance and Compliance
Data privacy and security laws exist to protect people’s information. Ingestion processes should adhere to these laws early on. Meanwhile, data governance challenges may include inconsistent data definitions, lack of metadata management, and insufficient access controls.
Consequences: Regulatory fines, legal penalties, and damage to brand reputation.
Complexity and Maintenance
It is hard to maintain and troubleshoot complex data ingestion pipelines. It may come from legacy systems, lack of documentation, and overly customized solutions.
Consequences: Increased IT support costs, longer time to address issues, and reduced agility.
What are the Best Practices in Data Ingestion?
Pulling and collecting data is problem-free when done right. The following are the best practices for data ingestion.
Define Clear Objectives
Requirements and objectives should be clear from the start. So, align the types of data to ingest, its sources, and intended use to business goals.
Data Quality Assurance
As mentioned earlier, data ingestion is not just copying data to a target location. The copy needs to be validated for accuracy, completeness, and consistency. The ingestion process should check for missing values, duplicate rows, and other data quality checks.
Design for Scalability and Performance
Design the ingestion process to reduce latency and maximize throughput. Use approaches like incremental updates, parallel processing, and compression techniques. Then, choose scalable architectures, distributed processing frameworks, and cloud-native services to handle large-scale ingestion.
Data Security and Privacy
Protect sensitive data using encryption, data masking, and access controls during ingestion. Do this on data at rest and data in transit. Review standards and data privacy regulations to ensure this is done. Get some assistance or consulting services if needed.
Metadata Management
Catalog information about the data source, format, schema, lineage, and traceability. Document data flows, dependencies, processing steps, and transformations for transparency and compliance.
Alerting and Monitoring
Alert systems proactively detect problems like errors in ingestion processes. Note how long each ingestion pipeline took to finish to track performance bottlenecks. Procedures to address the issues should exist and be signed by all parties involved.
Documentation
Encourage knowledge sharing, ease troubleshooting, and address personnel movement through proper documentation. Documentation should include updated requirements, goals, objectives, metadata, and others mentioned earlier. This will support ongoing maintenance, support, and enhancements.
Continuous Improvement
Evaluate and improve data ingestion processes based on feedback, performance metrics, and evolving business needs. Identify opportunities to improve the efficiency and effectiveness of the current ingestion processes.
What are the Data Ingestion Steps?
The steps here assume you already have a requirements document and others mentioned in the best practices section. Once this is available, the technical part is creating the ingestion process which involves the following:
1. Source Identification
These involve only the data sources that are relevant to the current requirements. It can include databases, files, APIs, streaming platforms, or other data repositories. Get the credentials to access them. Then, form the connection strings, file paths, URL of the APIs, or other ways to open each data source. Once connected to the sources, identify the tables or objects where the data resides.
2. Data Extraction
After identifying the data sources, the next step is to extract them. This can involve various techniques depending on the source type. Examples are querying the tables from a database using SQL, reading files from storage, or subscribing to streaming data feeds. The extraction process creates a copy of the data from the sources.
3. Data Transport
After extraction, the data is now in transit. The transport involves transferring data over an internal network, or the internet. This requires network protocols, security certificates, and encryption methods to move the data securely.
4. Optional Data Transformation
Sometimes, the source data types are not compatible with the targets. This needs minor transformations like converting a date and time information to character strings or vice-versa. For example, mobile apps may use SQLite to store information. Dates and times in that SQL database are stored as character strings. To ingest the data from SQLite to a PostgreSQL database, date and time strings can be converted to PostgreSQL timestamp.
5. Data Loading
6. Data Validation
After loading, it’s important to validate the ingested data by performing quality checks as stated in the best practices earlier. If the data ingestion tool has a schema and data comparison feature, use it for validation. Otherwise, you can use SQL scripts or any other way to compare the source and targets.
7. Monitoring and Error Handling
Technical problems can happen at any time and this can trigger runtime errors during ingestion. If your tool has an exception handling feature, use it to manage errors. Moreover, if it includes a logging mechanism, monitor the logs for possible issues.
After performing the above steps, the ingested data is ready for further processing.
Conclusion
Data ingestion is a crucial part of any data management process. A database acting as a staging area to store various data sources can be useful in processing a data warehouse. The collected data can also be inputs to machine learning and artificial intelligence.
As you have also seen, data ingestion overlaps with ETL by having it done first before ETL proceeds. Depending on your organization’s need and capability, you can choose from batch, real-time, or micro-batching to approach your data ingestion processes.Be aware of the challenges and avoid the myths. Instead, use best practices and data ingestion steps outlined earlier. Challenges have a cost or consequences so careful planning is crucial. Nevertheless, data ingestion is not an impossible feat. Being good at this will open opportunities for more fun and challenging data projects worth doing.