Best Google Cloud ETL Tools
Google Cloud ETL Tools
Data is generated in real-time and from a wide range of sources including mobile apps, websites, IoT devices, etc. Capturing, processing, and analyzing to get insights is a priority for all enterprises. However, this data is usually not in the format suitable for analysis or effective use downstream, and this is where ETL comes in.
Extract, Transform, and Load (ETL) refers to a series of processes that map your data’s journey from its sources to the warehouse. The implementation of ETL involves bringing in different varieties of data from different sources, curating the data, and loading the curated data into another data source.
ETL enables organizations to have accurate data based on their specific application consolidated in one place. This gives them the perfect ground to draw insights from this data via analysis and reporting. Google Cloud has a variety of powerful ETL tools that ensure you don’t have to do ETL manually and compromise the integrity of your data. These include data preparation, pipeline building and management, and workflow orchestration tools. Let’s have a closer look at the favourite ETL tools from Google Cloud Platform.
Cloud Data Fusion
Cloud Data Fusion is a code-free, cloud-native data integration solution. It is fully managed by Google, taking away the burden of infrastructure provisioning and management from ETL developers. Data Fusion has a graphical interface where ETL developers can easily deploy ETL data pipelines without writing a single line of code.
In addition, it has been built with an open-source core, CDAP, which ensures pipeline portability between hybrid and multi-cloud environments. It has a library of 150+ preconfigured plugins for added functionality at no cost. Since Data Fusion provides a unified platform for data wrangling and pipeline design, it improves the collaboration of business and IT.
Dataflow
Dataflow is a serverless, fast, and cost-effective service for processing both stream and batch data. Just like Data Fusion, Google takes care of infrastructure provisioning and cluster management. If your organization deals with data that is being continuously generated together with that which has been stored over a period, Dataflow would suit your needs. It allows users to build pipelines using Apache Beam SDK together with either Python or Java. These pipelines are then deployed and executed as Dataflow jobs. Dataflow recruits virtual machines to execute the data processing. You don’t have to worry if your traffic pattern is irregular, Dataflow seamlessly autoscales to increase the number of instances when traffic spikes.
Dataprep
Dataprep by Trifacta is a code-free data wrangling solution to prepare data for downstream processes such as analytics. Dataprep takes care of data discovery, structuring to the usable format, cleaning the data, augmenting data to enrich it, validating, and publishing the data. Dataprep has built-in data quality assessment and validation tools to enable you to work with high-quality data. With Dataprep, you can automatically build data engineering pipelines without writing a single line of code. Then you can proceed to leverage Dataflow for processing at scale and BigQuery for storage.
Dataproc
Dataproc is Google Cloud’s serverless solution for open-source data processing at scale via Apache Spark & Flink, Presto, etc. It has support for over 30 open source tools and frameworks to facilitate this. It is fully managed by Google, relieving users from the operational overhead of infrastructure management. It is natively integrated with Vertex AI, Dataplex, und BigQuery to support intelligent ETL.
With Dataproc, you can build data science and ELT models a lot faster as a result of integration with Vertex AI workbench and easily incorporate big data processing. To provide job portability, you can use Dataproc on Kubernetes.
Cloud Composer
Cloud Composer is a fully managed workflow orchestration service that works with different services on GCP for ETL. It has been built on Apache Airflow, and it allows users to create Apache Airflow jobs and run them on Dataproc clusters. You can use Cloud Composer to launch Dataflow ETL pipelines and orchestrate Data Fusion pipelines including other custom tasks performed outside it. Cloud Composer can also be used to automate Dataprep flow migration between workspaces.
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