Top Challenges of Data Ingestion Pipelines and How to Overcome Them with Google Cloud

3 min read
Jun 20, 2023

Modernizing your data management can help your organization leverage its data for sophisticated applications like machine learning and advanced analytics. But this requires bringing disparate data sources together in a cloud-based data warehouse or lake, which can be mined for insights.

Theoretically, it sounds simple enough: build a pipeline to transport siloed or disparate data into a cloud-based data warehouse or lake for processing. But this is a significant undertaking, especially with the growing volume, velocity, and variety of data. Done incorrectly, it can negatively impact data quality, accuracy, and usability.

Common issues with data ingestion pipelines

Here are some typical challenges encountered when developing cloud-native pipelines—and what you can do to alleviate these challenges.

Data quality 

High-quality data leads to better decision-making—and vice versa. The data ingestion process can include inconsistent, incomplete, or out-of-date data, all of which can lead to errors or inaccuracies. As the adage says, garbage in, garbage out. 

It’s critical to ensure robust data quality controls during this process, such as data cleansing, transformation, mapping, integration, and validation.

Data variety

There’s also the issue of data variety, which requires transforming various data formats before they can be integrated with other data sources. Data variety and formatting can be an issue whether you’re building an ETL pipeline (extract, transform, and load), which typically transforms data in a staging area, or an ELT pipeline (extract, load, and transform), which transforms data in the storage layer.

Real-time ingestion

If data is processed as it’s being generated for analytic applications in real-time without a scalable infrastructure, it could cause bottlenecks or even failures during ingestion. 

Resolving this could require scaling network bandwidth, using techniques such as parallelization or data compression, or redesigning the pipeline to handle the increased volume and velocity of data.


During ingestion and while in transit, malicious actors could intercept or expose sensitive data. Always use secure protocols and channels for data transfer, and perform data checks throughout the ingestion process. Monitoring can also help to identify any anomalies, errors, or breaches.

If you’re dealing with sensitive or personally identifiable information (PII), ensure it’s secure across the various stages of the ingestion process. Otherwise, you risk noncompliance, data breaches, regulatory fines, and reputational damage. Using a data governance and management framework can help to enforce security and compliance policies during ingestion and transit.

Google Cloud tools for secure data ingestion

Writing code to ingest data and then manually mapping that data is time-consuming and tedious. Fortunately, Google Cloud offers several interface-based data ingestion services to minimize the need for coding and reduce development time.

For example, Google Cloud’s BigQuery Data Transfer Service allows you to ingest data from SaaS applications, data warehouses, and external cloud storage providers (including Amazon S3 and Azure Blob Storage). You can also use third-party transfers for external data sources, such as Salesforce CRM and Adobe Analytics, available in Google Cloud Marketplace.

For code-free deployment of ETL/ELT data pipelines, Cloud Data Fusion offers a point-and-click interface, making it easier to design and manage advanced pipelines. Built with an open-source core for portability, it includes more than 150 pre-configured connectors and transformations.

You can also use pre-existing Dataflow templates for common data ingestion scenarios. Or, you can opt for a managed service to deploy code, such as Dataflow or Dataproc. For open data integration, Google Cloud Cortex Framework allows you to connect data from various sources (private, public, and commercial) and offers predefined BigQuery models for SAP or Salesforce data.

Many options exist, but coding could still be required if a connector isn’t available for a particular source system. Regardless of which option you choose, ensuring ingestion is secure at every stage of the process is critical.

A trusted partner to help every step of the way

Pythian is data and analytics in the cloud. That means, no matter where you are in your data journey, Pythian has the services to ensure you can migrate, modernize and transform your data in a safe, secure, and scalable manner. 

Contact a Pythian Google Cloud expert and see how we can help you meet your goals.


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