3 most popular data ingestion tools
Tags: data, tooling, tutorial, datapipeline
Published: 2023-02-15 18:04:08

##What are data ingestion tools?

Data ingestion tools are software programs or systems that are used to bring data into a central repository, such as a data lake or a data warehouse. The benefits of using data ingestion tools include:

**Automation:** Data ingestion tools automate the process of collecting, transforming, and loading data, which can save time and reduce errors compared to manual data entry.

**Scalability:** Data ingestion tools can handle large volumes of data, making them suitable for use in big data environments.

**Flexibility:** Data ingestion tools can be configured to handle different types of data sources and formats, including structured, semi-structured, and unstructured data.

**Data Quality:** Data ingestion tools can apply data validation, data cleansing, and data transformation rules to ensure the quality of data before it enters the data lake or data warehouse.

**Real-time:** Some data ingestion tools can handle real-time data, which allows organizations to make timely decisions based on the most up-to-date information.

**Monitoring:** Many data ingestion tools provide monitoring and logging capabilities, which can help organizations troubleshoot issues and track the flow of data through the system.


##Popular tools for Data Ingestion


###Apache Kafka

Apache Kafka is an open-source big [data ingestion](https://www.simform.com/blog/data-ingestion/) software licensed by Apache. It is commonly used for high-performance data pipelines, streaming analytics, and data integration.

The platform is renowned for its exceptional throughput and low latency capabilities. By leveraging a group of machines, Apache Kafka can provide network-limited throughput and achieve latencies as low as 2ms.

Apache Kafka is implemented in Scala and Java and can connect with external systems for data import and export via Kafka Connect. Due to its open-source nature, the platform boasts a vast ecosystem of community-driven tools that users can use to extend its functionality.

###Improvado

Improvado is a comprehensive data ingestion solution designed specifically for marketing purposes. Its core functionality is to automate repetitive data tasks, allowing marketing analysts to focus on their primary duties.

The Improvado platform is capable of streamlining data from over 200 marketing data sources and offers pre-configured data extraction patterns to expedite data extraction once the platform is integrated.

After data is extracted, the platform applies an MCDM normalization framework based on custom metrics or parameters to transform siloed data, which can then be ingested into a data warehouse. Improvado performs batch ingestion, with a data synchronization frequency of one hour.

Once data is normalized and organized within the warehouse, it can be pushed to any business intelligence tool. Visualization is an essential aspect of marketing performance analysis and tracking changes in marketing metrics. Automated marketing reports are available to all employees throughout the company, promoting collaboration between different departments.


###Wavefront

Wavefront is a cloud-hosted, high-performance streaming analytics service that enables users to ingest, store, visualize, and monitor all forms of metric data. The platform's exceptional scalability allows it to handle very high query loads and data ingestion rates, processing millions of data points per second.

Users can collect data from over 200 sources and services, including cloud service providers, DevOps tools, big data services, and more.

With Wavefront, users can view data in custom dashboards, receive alerts on problem values, and perform advanced functions such as anomaly detection and forecasting.

##Conclusion

Data ingestion tools play a crucial role in maintaining a healthy data ecosystem. By automating data flow, organizations can unlock previously unexplored opportunities and gain a fresh perspective on their data. Data ingestion allows analysts to minimize the time spent on routine data operations and focus on conducting meaningful analysis.