Data ingestion collects and organizes raw data from various sources to help in decision-making. Think streaming sites knowing exactly what show to recommend next, or e-commerce sites suggesting products with near-telepathic accuracy.
For go-to-market (GTM) teams especially, data ingestion processes are the backbone of everything from lead scoring to managing customer lifecycles. When your data flows smoothly from source systems into your data warehouse or data lakes, your sales and marketing teams can make data-driven decisions immediately instead of waiting around for reports that are outdated by the time they get to them.
What Is Data Ingestion?
Data ingestion is the process of importing, transferring, or loading data from different sources — like your CRM, social media, IoT devices, or data enrichment APIs — and compiling it into a storage system where it can be analyzed and transformed into valuable insights. Data ingestion handles both structured and unstructured data, all while maintaining data quality so that you and your analytics platforms get valuable, concrete numbers and insight every time.
Why Data Ingestion Matters in the Modern Data Ecosystem
Without effective data ingestion, business decision makers would be flying blind. Data ingestion allows companies to aggregate information from multiple sources, creating a comprehensive, unified view of both the market and your business’s standing.
The speed and reliability of your data ingestion pipeline directly impact how quickly your teams can respond to market changes, identify new opportunities, and optimize their workflows.
The Difference Between Data Ingestion and Data Integration
While data ingestion and data integration often get used interchangeably, they serve different purposes.
Data ingestion focuses on the intake process and gets raw data from outside into your data analytics platforms as efficiently as possible.
Data integration, on the other hand, organizes that data by applying complex data transformation rules and creates comprehensive datasets that can help predict trends in the market and improve your business’s health.
How Data Ingestion Works
Data ingestion typically follows four key steps that transform data into actionable information:
Data Collection: Data connectors pull information from a wide range of targeted sources.
Transformation/Cleansing: The data is then analyzed for quality and standardized to ensure consistent formats across different sources.
Routing/Storage: The processed data gets directed to data warehouses for storage or real-time processing engines for immediate analytics.
Analytics Access: Each dataset is then organized based on predetermined permissions so that only team members who need access to the data can view it.
Types of Data Ingestion
Businesses rarely rely on just one type of data ingestion. Instead, they mix and match approaches based on their specific needs and goals.
Batch Ingestion
Batch ingestion processes data in scheduled intervals or chunks. This method is ideal when you have large volumes of data from your CRM or ERP systems that don’t require immediate processing, like daily sales reports, monthly financial summaries, or periodic data warehouse updates.
Most ETL tools use batch processing because it’s resource-efficient and allows for more complicated data transformations that would be challenging to handle in real-time.
Real-Time / Streaming Ingestion
Streaming ingestion processes data continuously as it arrives. From app usage tracking to financial transactions, the immediacy of streaming data makes it ideal for real-time decision-making and security.
The trade-off is higher complexity and pricing, but when time is of the essence, streaming data ingestion becomes non-negotiable.
Hybrid Ingestion
If you want the best of both worlds, you can combine both batch and real-time approaches, creating a hybrid system. Hybrid ingestion could use streaming for critical real-time analytics while handling bulk historical data through batch processing.
This flexibility enables teams to optimize and streamline their data ingestion pipelines according to their specific business needs, without being confined to a standardized approach.
Common Data Sources for Ingestion
Successful companies aren’t necessarily the ones with the best products. They’re the ones that are ingesting data from every possible touchpoint. The most common types of data sources that can open your business to all-new insights include:
Cloud Data and Applications: Salesforce’s Data Cloud, HubSpot, and other SaaS platforms store important information you can take advantage of.
Databases: MySQL, PostgreSQL, and other SQL databases contain transactional and operational data.
Data Warehouses: Snowflake, BigQuery by Google, and similar platforms serve as warehouses so that you can collect more data and do more with it.
APIs: RESTful services and GraphQL facilitate communication between different software systems, giving you access to third-party data and services.
IoT Devices: Sensors, smart devices, and other connected tech generate continuous streams of operational data.
Files and Logs: CSV exports, JSON feeds, and other file-based data sources contain valuable information that needs regular processing.
Challenges in Data Ingestion
Data ingestion isn’t always smooth sailing. Even the most well-designed systems run into roadblocks that can disrupt data flow and impact analytics performance. The good news is that ZoomInfo and our data management tools can help you avoid them.
Latency and Throughput Issues
When your data ingestion pipeline experiences even minor latency, you’ll end up with delays that cascade through your entire analytics ecosystem. Bottlenecks often occur at integration points, during data transformation, or when target systems can’t handle the volume of incoming data.
Schema Drift and Inconsistent Formats
Nothing breaks a data pipeline faster than unexpected changes to how the data is formatted and organized. This so-called “schema drift” is particularly challenging when dealing with multiple data sources that update or restructure independently, and it can turn reliable datasets into messy collections of inconsistent information.
API Limits and Connectivity Problems
Third-party APIs are subject to rate limits and throttling mechanisms as well as occasional outages, all of which can disrupt your data ingestion workflows. Ultimately, these problems limit how much data you’re able to intake, leaving you working with an incomplete dataset.
Data Silos and Fragmentation
When different departments within your company use separate systems that don’t talk to each other, you end up with isolated datasets that are hard to analyze and combine.
Breaking down data silos through internal data ingestion should be a top priority for any business wanting to leverage all its internal data to the fullest extent possible.
Data Ingestion Tools and Technologies
The number of data ingestion tools has exploded in recent years, with options ranging from drag-and-drop ETL platforms to sophisticated open-source frameworks that handle massive streaming workloads. Choosing the right tools depends on your specific business goals, expertise, and budget.
Popular ETL/ELT Tools
A variety of ETL (Extract, Transform, Load) and ELT tools are available to support different data integration needs. Here are some widely used options and their key use cases:
Apache NiFi: A visual data flow platform that excels at routing, transforming, and monitoring data flows with real-time processing and extensive security features.
Fivetran: Automated data pipeline platform that handles schema changes and provides pre-built connectors for over 700 SaaS applications without requiring coding expertise.
Talend: An enterprise-grade data integration platform offering both cloud and on-premises options with advanced data transformation capabilities for complex business rules.
Stitch: Simple, developer-friendly ELT service that copies data from various sources to cloud data warehouses with minimal setup and configuration.
Airbyte: Open-source ELT platform with a growing library of customizable connectors.
Cloud-Native Ingestion Tools
Cloud-native ingestion tools are designed to integrate with specific cloud ecosystems, offering scalable, managed solutions for data movement and transformation. These tools are optimized for performance, reliability, and tight integration with other cloud services:
AWS Glue: Amazon’s serverless ETL service that automatically scales based on workload demands and integrates seamlessly with other Amazon services.
Azure Data Factory: Microsoft’s cloud-based data integration service that provides hybrid connectivity between on-premises and cloud systems.
Google Cloud Dataflow: Stream and batch processing service that handles both real-time and historical data processing with automatic scaling.
Open-Source Tools
Open-source tools offer flexibility and control for teams looking to build custom data ingestion pipelines tailored to their specific requirements. These tools are particularly suited for organizations that need control over data flow, processing logic, and infrastructure setup. Some popular open-source options include:
Apache Kafka: A streaming platform ideal for real-time data pipelines, handling millions of events per second. Kafka excels at handling high-throughput, low-latency data ingestion and supports both pub/sub and message queue use cases.
Logstash: A data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and sends it to various destinations like Elastic.
Fluentd: Filters and forwards log data from various sources with a plugin-based design for maximum flexibility.
ZoomInfo’s Real-Time Data Ingestion Capabilities
ZoomInfo’s platform transforms how go-to-market teams handle B2B data ingestion, providing clean, actionable intelligence that powers sales and marketing teams without the typical headaches of managing multiple data sources:
Unified B2B Data Delivery: Unprecedented access to comprehensive firmographic information through standardized APIs and data formats.
Flexible Ingestion Options: Support for both real-time streaming and batch processing to match your specific workflow and goals.
Salesforce & Marketing Stack Integration: Pre-built connectors that automatically sync data with your existing CRM and marketing automation platforms.
Automated Enrichment & Update Cycles: Continuous data validation and enhancement while purging redundancies to keep your datasets fresh and accurate.
Built for GTM Execution: Data ingestion is designed specifically for go-to-market activities including sales prospecting, account-based marketing, and revenue operations workflows.
Best Practices for Effective Data Ingestion
Building reliable data ingestion pipelines requires more than just organization and pattern-finding. The difference between good and great data ingestion comes down to thoughtful planning around data quality, security, and operational efficiency to prevent problems before they start.
Monitor Data Quality
Implementing strong validation checks throughout your data ingestion pipeline helps catch issues early. Nothing is more important than knowing how to maintain quality data. Set up automated scans for accuracy, completeness, and accessibility, while tracking exactly how data flows through your systems.
ZoomInfo can monitor, identify, and resolve inconsistencies across your complex B2B datasets.
Secure and Compliant Data Handling
Data security can’t be an afterthought. Breaches can ruin the reputation of your company, so you need to ensure all data is encrypted and meets regulatory requirements like GDPR, EU privacy laws, and HIPAA.
Set strict permissions that limit who can access the data, and make sure your platform is set up to automatically delete information after the end of the legal retention period.
Automate Wherever Possible
Manual data ingestion processes don’t scale easily and introduce the unnecessary risk of human error. Instead, invest in automation tools that handle routine tasks like schema drift and pipeline monitoring.
Be sure to set up automated alerts for data quality issues. These automatic data models allow your datasets to grow without losing clarity.
Empower Your GTM Team With ZoomInfo’s Connected Data Ingestion Solution
The future of go-to-market success belongs to teams that can turn data into action faster than their competition. Instead of wrestling with fragmented datasets and inconsistent data quality, ZoomInfo’s data ingestion provides seamless integration with nearly 100 partners, including AWS, to support your existing cloud infrastructure and CRM. Let your data engineers focus on implementing innovative, analytic solutions while we handle the accuracy and quality of your data.
When your data flows smoothly from ingestion to insight, every interaction becomes an opportunity to accelerate revenue and growth. Ready to transform your data ingestion strategy? At ZoomInfo, we are proud to be the reliable, scalable foundation of over 250 million users, and we can help your GTM team make data-driven decisions in real time that outmaneuver the competition.