Introduction
Digital Marketing has begun its evolution across the world by reshaping business planning and marketing strategy. It significantly improves website traffic and business sales through online marketing.
Although 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 Google Analytics ETL tools.
Table of Contents
What is Google Analytics?
Google Analytics is a cloud-based, open-source analytical tool provided by Google to analyze and report website traffic and customer behavior. The comprehensive dashboard and ease of integration with different platforms make it a popular choice for industries that drive their business decisions on customer behavior and interaction.
Features Of Google Analytics
- It has excellent capability to collect the details automatically and generate extensive analytical reports.
- It enables you to understand customer behavior, their interaction on your products, the reasons for bouncing off, source platform, lead generation, and many more.
- It allows you to study the competition, helps create the content which can engage customers, and segment the customer base.
To explore Google Analytics, visit the official website here.
What is ETL?
ETL stands for Extract, Transform, and Load. It is a process of extracting data from persistent sources (RDBMS, Web Client, FTP server, cloud storage, social media, etc.), transforming them by applying functions, calculations, and then loading it onto the target database or data warehouse or onto a reporting tool to generate analytics.
As the ability of businesses to collect data explodes, data teams have a crucial role to play in fueling data-driven decisions. Yet, they struggle to consolidate the data scattered across sources into their warehouse to build a single source of truth. Broken pipelines, data quality issues, bugs and errors, and lack of control and visibility over the data flow make data integration a nightmare.
1000+ data teams rely on Hevo’s Data Pipeline Platform to integrate data from over 150+ sources in a matter of minutes. Billions of data events from sources as varied as SaaS apps, Databases, File Storage and Streaming sources can be replicated in near real-time with Hevo’s fault-tolerant architecture.
What’s more – Hevo puts complete control in the hands of data teams with intuitive dashboards for pipeline monitoring, auto-schema management, and custom ingestion/loading schedules.
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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.
Even though Google Analytics provides thorough reports on the website traffic, sometimes it is required to extract the data to perform a deeper level of analytics.
We have listed down some best Google Analytics ETL tools available in the market to perform Google Analytics ETL over data. Click on the links to traverse the post quickly.
- Hevo Data
- Talend
- Apache Spark
- Informatica PowerCenter
- Apache NiFi
1. Hevo Data
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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.
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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 -: Get access to near real-time replication on All Plans. Near Real-time via pipeline prioritization for Database Sources. For SaaS Sources, near real-time replication depend on API call limits.
- In-built Transformations – Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface, or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation.
- Monitoring and Observability-: Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ETL with Alerts and Activity Logs.
- Reliability at Scale -: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency.
- 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day free trial.
Hevo Data provides Transparent Pricing to bring complete visibility to your ETL spend. You can also choose a plan based on your business needs.
Pricing Model of Hevo Data
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Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow. Simplify your Data Analysis with Hevo today!
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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. Read more about pricing here.
![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.
Pricing
Apache Spark is free to use, and you can download it from here.
However, distributions like Cloudera, Hortonworks charge for the support. You can check the Pricing here.
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 here.
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.
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With Hevo, you can get started in just a couple of minutes as all you need to do is select your data source, provide the user credentials to the data source, and the Hevo platform will do the rest in extracting the data and loading it into the specified destination.
Want to take Hevo for a spin?
SIGN UP and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
What are your experiences with Google Analytics ETL tools? Share your experiences. We would love to hear from you in the comments!
Vishal has a passion towards the data realm and applies analytical thinking and a problem-solving approach to untangle the intricacies of data integration and analysis. He delivers in-depth researched content ideal for solving problems pertaining to modern data stack.