Google Analytics has an extraordinary report generation capability based on customer interaction, a case may arise where you need to perform a notable in-depth analysis of the data.
If you are looking to conduct such analysis, well look no further! This blog introduces you to some of the best GA ETL tools.
Top 5 Google Analytics ETL Tools
- Hevo Data
- Talend
- Apache Spark
- Informatica PowerCenter
- Apache NiFi
1. Hevo Data
- Hevo Data, a No-code Data Pipeline helps you to replicate data from any data source with zero maintenance.
- You can get started with Hevo’s 14-day Free Trial and instantly move data from 150+ pre-built integrations comprising a wide range of SaaS apps and databases. Using Hevo, you can precisely control pipeline schedules down to the minute.
- Setting up data pipelines with Hevo is a simple 3-step process by just selecting the data source, providing valid credentials, and choosing the destination.
- Hevo not only loads the data onto the desired Data Warehouse but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Check out what makes Hevo amazing:
- Near Real-Time Replication
- In-built Transformations
- Monitoring and Observability
- Reliability at Scale
- 24×7 Customer Support
Pricing Model of Hevo Data
Pricing that is adaptable to your needs. Clear, simple, and predictable.Pick the ideal plan for your team based on your requirements.
2. Talend
- Talend is a widely used ETL tool in the industry, available in both community version (Open studio) and Enterprise version (Talend Cloud) with an excellent interface. It offers features like Data Integration, Big Data Integration, Data Preparation to perform Extract Transform and Load.
Key Features Of Talend
- The Talend app contains an interactive space to perform ETL operation using available functions (called palette), which can be dragged and dropped to the drawing area. Various customization options are available as per function needs.
- Talend has a custom component called tGoogleAnalyticsInput to connect to Google Analytics to fetch the data.
- Talend generates the Java code when the workflow is compiled and uses Apache Spark for in-memory computation.
- The workflows then can be published to any fully managed cloud services.
- Talend has excellent support for API Services, Data Stewardship, Data Inventory, and B2B.
Talend Use Case
- Talend has vast offerings for multiple sectors to perform agile ETL with the data. Industry domains like Financial Services, Government applications,
- Risk and Compliance, Data Privacy use Talend to deliver analytics on their data.
Pricing
Talend’s base price starts from $12,000 a year and has multiple categories for pricing.
![Download the Guide to Evaluate ETL Tools]()
Download the Guide to Evaluate ETL Tools
Learn the 10 key parameters while selecting the right ETL tool for your use case.
3. Apache Spark
Apache Spark was developed under the Apache foundation and is an open-source unified analytics engine for large-scale data processing and analysis. It works on the fundamentals of Hadoop MapReduce with in-memory calculations.
Key Features Of Apache Spark
- Apache Spark has excellent capability to process large volumes of data by parallelizing in memory with the help of its executor and cores.
- Apache Spark is available in integrated platforms like Cloudera, Hortonworks, MapR, and Databricks.
- It can be installed on Standalone and Clustered mode.
- Apache Spark can extract data from Google Analytics and perform ETL by using an external connector – CData Google Analytics Connector. You can read more here.
- Apache Spark uses functional programming, therefore you need to be conversant with Java, Scala, and Python coding languages.
- Apache Spark is capable of effectively performing real-time and batch processing and has extensive support for Machine Learning libraries.
4. Informatica PowerCenter
Key Features Of Informatica PowerCenter
- Informatica PowerCenter is an on-premise ETL tool and has extensive capability to extract data from various sources and perform ETL.
- Informatica PowerCenter comes with Google Connector to connect to Google Analytics objects as a source in mapping tasks.
- Informatica can easily handle rapid prototyping, profiling, and batch-based operation.
- It has the excellent capability of data transformation of structured and unstructured data, including PDF, Excel, IoT data, etc.
- It has real-time connectivity to provide accurate and timely data for analysis.
Pricing
- The cloud version of the Informatica PowerCenter starts at $2000 per month for its most basic plan. Cloud providers like AWS and Azure provide this as a completely managed service on a pay-as-you-go model.
5. Apache NiFi
Key Features Of Apache NiFi
- Apache NiFi is an open-source ETL tool to perform data analysis from the persistent source and destination.
- It offers seamless integration with MySQL via connectors available.
- Apache NiFi, with the help of connectors, extracts the data and performs complex transformations by creating flow files.
- Apache NiFi can extract the data from Google Analytics by using an external connector – CData Google Analytics Connector. You can read more here.
- Apache NiFi has a web-based interface that allows users to do seamless design, control, and monitor the process.
Pricing
- BatchIQ provides Apache NiFi, and AWS Marketplace has simple integration. More pricing details can be found Apache NiFi website.
How To Choose The ETL Tool For Google Analytics?
Choosing the appropriate ETL tool to perform the analysis will solve half of the problem. We have listed a few aspects to help you decide.
- Easy to Install/Integrate: An ETL tool should be user-friendly that can easily integrate with the existing architecture. Cloud-based tools are most beneficial in terms of integration and deployment of the pipeline to production.
- Monitoring and Management: ETL tools perform a continuous operation on the pipeline, which needs to be monitored and controlled. It should have proper supervision and controlling mechanisms to send alerts in case of failure.
- Wide Range of Data Transformation: Any ETL tool’s critical aspect is to possess vast libraries of functions to perform calculations and transform the data from various sources to derive the analytics when loaded into the data warehouse. Business entirely depends on the metrics that denote success or failure.
- Real-Time Streaming: In this fast-paced world, everyone wants to analyze the data in real-time or near real-time. A right ETL should have the capability to generate reports on analysis in real-time.
- Data Security and Reliability: This is a crucial feature to consider to provide security to the data over the internet. Hence, the ETL tool should hold the data securely and be able to reliably load data to your target database and recover from unexpected failures.
Conclusion
- There are various options available in the market when it comes to Google Analytics ETL tools. Each of them has a set of features and related pros and cons.
- When it comes to fully managed ETL, you can’t find a better alternative than Hevo. It is a No-code Data Pipeline product that will help you move data from 150+ data sources including 40+ Free sources such as Google Analytics to your destination. It is straightforward to set up as you can get the tool up and running in a jiffy.
Vishal Agarwal is a Data Engineer with 10+ years of experience in the data field. He has designed scalable and efficient data solutions, and his expertise lies in AWS, Azure, Spark, GCP, SQL, Python, and other related technologies. By combining his passion for writing and the knowledge he has acquired over the years, he wishes to help data practitioners solve the day-to-day challenges they face in data engineering. In his article, Vishal applies his analytical thinking and problem-solving approaches to untangle the intricacies of data integration and analysis.
