Introduction
In the digital age, data is often hailed as the new gold. Businesses, from startups to large enterprises, rely on data to make informed decisions, gain insights, and fuel innovation. However, to harness the full potential of data, it must first be collected and ingested into storage using a data pipeline. This critical data ingestion process in data engineering has challenges and complexities. This blog will explore significant hurdles and best practices for data ingestion.
Data Ingestion
The process of gathering data from diverse sources and transferring it to your data warehouse or data lake for subsequent processing and analysis is known as Data ingestion. It serves as the initial phase in contemporary data management workflows.
Although the concept of moving data from one point to another seems straight, the reality is that data ingestion comes with various challenges that can hinder the overall quality and usability of your data.
In this blog, we will identify and discuss the significant data ingestion challenges and provide the best practices to empower data orchestrators in surmounting these obstacles.
What is Data Ingestion?
Data ingestion is the process of gathering and importing data, making it available for immediate or future use. This process can involve either batch processing or real-time stream processing.
Data ingestion involves transferring data from diverse sources into a common destination, providing data analysts with a centralized platform for analysis.
For many organizations, the sources of data ingestion are extensive and diverse, ranging from traditional spreadsheets and web scraping tools to IoT devices, proprietary applications, and SaaS platforms, among others. This data is typically ingested into an on-premises or cloud-based data warehouse or data lake, where it can effectively extract valuable information and insights.
Without the process of data ingestion into a centralized repository, an organization’s data becomes confined within isolated silos. It forces data consumers to engage in the tedious task of accessing each source system or SaaS platform individually, offering only a fraction of the larger picture. As a result, decision-making becomes slow and less accurate.
Data ingestion may sound straightforward in theory, but in practice, it is complex. The increasing volumes and varieties of data sources, often called “big data,” pose an ongoing challenge for data collection, consolidation, and transformation to make it cohesive and usable.
For instance, many APIs export data in JSON format. During the ingestion process, it becomes necessary to not only transfer this data but also apply slight transformations to format it into a tabular structure suitable for loading into the data warehouse. Other common transformations conducted during data ingestion include data formatting and deduplication.
To alleviate some of this complexity, data ingestion tools with automated connectors that facilitate the collection and transfer of data between source and target systems without requiring advanced coding skills like Python can be a valuable resource.
What are the two types of Data Ingestion?
The two types of Data Ingestion are Batch processing and Stream processing. Every data ingestion framework serves a unique purpose regarding the time it takes to ingest and make incoming data operational.
Batch
Batch data ingestion is the most widely used approach when organizations do not require immediate, real-time access to data insights. This method transfers data in batches at regular intervals from the source to the destination.
Batch data ingestion proves beneficial for data users who regularly generate reports, such as sales teams providing daily pipeline updates to their CRO. For such use cases, real-time data is unnecessary, but dependable and recurring data access is essential.
In some scenarios, data teams opt for micro-batch strategies, where data pipelines ingest data every few hours or minutes.
The lambda architecture-based data ingestion is worth mentioning, which combines elements of both streaming and batch data ingestion. This hybrid model employs three data layers:
- The batch layer offers an organization’s data a comprehensive and accurate overview.
- The speed layer provides quicker real-time insights with slightly lower accuracy.
- The serving layer which amalgamates outputs from both layers.
It enables data orchestrators to access sensitive information swiftly while maintaining access to a more precise overarching batch layer.
Batch data ingestion tools include
- Fivetran – responsible for data delivery from source to destination.
- Azure Data Factory – is Azure’s cloud ETL service for scale-out serverless data integration and transformation.
- AWS Data Pipeline – a web service that helps you reliably process and move data between different AWS compute and storage services and on-premises data sources at specified intervals.
Streaming
Streaming data ingestion, as the name suggests, refers to real-time data transfer. This method utilizes change data capture (CDC) to continuously monitor transactions or redo logs, promptly transporting any modified data, such as a new transaction, an updated stock price, or a power outage alert, to the destination data cloud without disrupting the database’s workload.
Streaming data ingestion proves invaluable for organizations dealing with highly time-sensitive data. For instance, financial services companies constantly analyzing fluctuating market information or power grid companies that require real-time monitoring and response to outages benefit significantly from this approach.
Notable streaming data ingestion tools include:
1. Apache Kafka – an open-source event streaming platform.
2. Amazon Kinesis – a streaming solution provided by AWS.
3. Google Pub/Sub – a GCP service designed for ingesting streaming data into BigQuery, data lakes, or operational databases.
What are the five significant challenges in Data Ingestion?
1. Data Variety: In today’s world, data comes in structured, semi-structured, and unstructured formats. Integrating and harmonizing these diverse data types can be a significant challenge.
2. Data Volume: The exponential growth of data has overwhelmed many organizations. Ingesting and processing massive volumes of data in real-time or near real-time is complex.
3. Data Velocity: The speed at which data is generated and ingested is critical. Some use cases require data to be collected and processed in real-time, adding complexity to the ingestion process.
4. Data Veracity: Veracity is critical, as accurate or reliable data can result in flawed decision-making. Data engineers must employ rigorous validation processes to ensure data accuracy, particularly when handling diverse data sources and addressing issues like missing values, duplicates, and anomalies, essential for maintaining data integrity and gaining valuable insights.
5. Data Quality: Ensuring data accuracy, consistency, and reliability is another significant challenge. Low-quality data can lead to inaccurate insights and decisions.
6. Data Security: Protecting sensitive data during ingestion is paramount. Security breaches during data ingestion can have severe consequences.
Learn the 10 Best Practices for Data Ingestion
1. Data Governance: Establish robust practices to ensure data quality, consistency, and regulation compliance.
2. Use of Streaming: For real-time data ingestion, streaming technologies like Apache Kafka can be invaluable. They enable the processing of data as it’s generated.
3. Scalability: Choose scalable data ingestion tools and platforms that can handle growing data volumes without performance degradation.
4. Data Validation: Implement data validation processes to identify and rectify data quality issues during ingestion.
5. Metadata Management: Keep track of metadata associated with ingested data. It helps with data lineage, cataloging, and making data more accessible.
6. Error Handling: Develop robust error handling and retry mechanisms to manage issues that may arise during data ingestion.
7. Data Compression: Use data compression techniques to reduce the volume of ingested data, which can significantly improve performance.
8. Data Partitioning: When working with large datasets, consider partitioning data for more efficient querying and processing.
9. Monitoring and Logging: Implement comprehensive monitoring and logging to track the health and performance of data ingestion processes. It allows for quick identification and resolution of issues.
10. Data Security: Ensure data encryption and access control mechanisms are in place to protect data during ingestion.
Conclusion
In conclusion, data ingestion is the crucial first step in the data processing journey. Organizations must implement best practices and utilize appropriate tools and technologies to overcome the challenges of volume, variety, velocity, integrity, and data quality. By doing so, they can ensure that their data pipelines are efficient, reliable, and able to deliver the insights needed to drive business decisions in today’s data-driven world.
Want to know more about implementing best practices by streamlining the Data Ingestion process to enable efficient Data Engineering to get support from Prudent’s team of experts? Contact us for a discovery call today!