Creating Cloud-based Data Ingestion pipelines that replicate data from multiple sources into your cloud data warehouse can be a huge project that demands a lot of manpower. Such a massive undertaking might be intimidating, and it can be tough to know where to start preparing one. The Google Cloud Platform enters the picture at this point.
In this article, you will gain information about Data Ingestion Google Cloud. You will also gain a holistic understanding of Google Cloud, its key features, Data Lifecycle, its components, Data Ingestion and its types, Data Ingestion in Google Cloud and its types. Read along to find out in-depth information about Data Ingestion Google Cloud.
What is Google Cloud?
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Google Cloud Platform is a suite of public Cloud Computing services such as data storage, data analytics, big data, machine learning, etc. It runs on the same infrastructure that Google uses internally for its end users. With the help of Google Cloud Platform, you can deploy and operate applications on the web.
Key Features of Google Cloud
- Computing and Hosting: It allows you to work in a serverless environment, use a managed application platform, leverage container technology, and build your own cloud-based infrastructure.
- Storage Services: It offers consistent, scalable, and secure data storage in Cloud Storage. You will have a fully managed NFS file server in Filestore. You can use Filestore data from applications that run on Compute engine VM instances or GKE clusters.
- Database Services: Google Cloud Platform offers a variety of SQL and NoSQL database services. You can use Cloud SQL, which can be either MySQL or PostgreSQL. For NoSQL, you can use Firestore or Cloud Bigtable.
- Networking Services: While your App Engine manages networking for you, GKE uses the Kubernetes model to provide a set of network services to you. All these services can load balance traffic across resources, create DNS records and connect your existing network to your Google network.
- Big Data Services: This service will help you to process and query the big data in your cloud to get fast and quick answers. With the help of BigQuery, data analysis becomes a cakewalk for you.
- Machine Learning Services: The AI platform will provide you with a variety of machine learning services. To access pre-trained models optimized for a specific application, you can use APIs. You can also build and train your own large-scale models.
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What is Data Lifecycle?
The Data Life Cycle is the series of processes that a piece of data goes through from the time it is created or acquired to the time it is explored or visualised at the end of its useful life. In simple terms, it refers to the length of time that data has been stored in your system.
The Data Lifecycle comprises 4 steps. These are as follows:
- Ingest: The initial step is to get the raw data, which could be streaming data from devices, app logs, on-premises batch data, or mobile-app user events and analytics.
- Store: After the data has been extracted, it must be stored in a manner that is both long-lasting and easy to access.
- Process and analyse: The data is converted from its raw state into usable information at this stage.
- Explore and visualise: The final step is to transform the analysis’ findings into a format that can be used to draw conclusions and make business decisions.
At each stage, Google Cloud provides multiple services to manage your data. This means you can select a set of services tailored to your data and workflow.
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What is Data Ingestion?
Data Ingestion is the process of transporting data from one or more sources to a target site for further processing and analysis. This data can originate from a range of sources, including data lakes, IoT devices, on-premises databases, and SaaS apps, and end up in different target environments, such as cloud data warehouses or data marts.
Data ingestion is a critical technology that helps organizations make sense of an ever-increasing volume and complexity of data.
Types of Data Ingestion
There are three ways to carry out data ingestion. Those are as follows:
1) Real-time Data Ingestion
The process of collecting and sending data from source systems in real time utilising solutions such as Change Data Capture (CDC) is known as real-time data ingestion. Real-time processing does not categorise data in any way. Instead, each piece of data is loaded and processed as a separate object as soon as it is recognised by the ingestion layer.
For time-sensitive use cases, such as stock market trading or power grid monitoring, where companies must react quickly to new information, real-time ingestion is critical. When it comes to making quick operational choices and recognising and acting on fresh insights, real-time data pipelines are essential.
2) Batch-based Data Ingestion
Batch-based Data Ingestion is the practise of incrementally collecting data from sources and transferring it in batches at predetermined intervals. Simple schedules, trigger events, or any other logical ordering can be used by the ingestion layer to collect data. When enterprises need to acquire specific data points on a daily basis or just don’t need data for real-time decision-making, batch-based ingestion comes in handy. In most cases, it is less expensive.
3) Lambda architecture-based Data Ingestion
Lambda architecture is a data ingestion system that combines real-time and batch processing. Batch, serving, and speed layers make up the setup. The first two layers index data in batches, whereas the speed layer indexes data that hasn’t been picked up by the slower batch and serving layers yet. This continuous hand-off between levels ensures that data is queryable with minimal delay.
What is Data Ingestion in Google Cloud?
There are several options for performing Data Ingestion Google Cloud.
- Using APIs on the data provide: Leveraging Compute Engine instances (virtual machines) or Kubernetes to pull data from APIs at scale.
- Real-time streaming: Cloud Pub/Sub is best for this option.
- Large amounts of data on-premises: Depending on volume, the Google transfer appliance or GCP Online Transfer are the best options.
- Large volume of data on other cloud providers: Leveraging Cloud Storage Transfer Service for this purpose.
Types of Data Ingestion in Google Cloud
The different types of Data Ingestion Google Cloud are as follows:
1) Data Ingestion Google Cloud: Ingesting App Data
Data is generated in large quantities by apps and services. App event logs, social network interactions, clickstream data, and e-commerce transactions are examples of this type of data. This event-driven data can be collected and analysed to uncover user trends and provide useful business insights.
From the virtual machines of Compute Engine to the managed platorm of App Engine, to the Google Kubernetes Engine’s container management (GKE), Google Cloud offers a number of options for hosting applications.
When you host your apps on Google Cloud, you get access to built-in tools and processes for sending data to Google Cloud’s vast data management ecosystem.
For performing Data Ingestion Google Cloud by ingesting app data, you can consider the following examples:
- Writing data to a file: An application writes batch CSV files to Cloud Storage’s object store. The data may then be imported into BigQuery, an analytics Data Warehouse, for analysis and querying using the import function.
- Writing data to a database: A Google Cloud app writes data to one of the Google Cloud databases, such as Cloud SQL’s managed MySQL or Datastore and Cloud Bigtable’s NoSQL databases.
- Streaming data as messages: Pub/Sub, a real-time messaging service, receives data from an app. A second app that has been subscribed to the messages can store the data or process it right away in instances like fraud detection.
2) Data Ingestion Google Cloud: Ingesting Streaming Data
Streaming data is sent asynchronously, with no expectation of a response, and the individual packets of messages are tiny. Streaming data is frequently used in telemetry, which collects data from geographically separated devices. Streaming data can be utilised to fire event triggers, perform complicated session analysis, and feed machine learning algorithms.
For performing Data Ingestion Google Cloud by ingesting streaming data, you can consider the following examples:
- Telemetry data: Internet of Things (IoT) devices are network-connected gadgets that use sensors to collect data from their surroundings. Even if each device only sends one data point per minute, when that data is multiplied by a large number of devices, big data methods and patterns are quickly required.
- User events and analytics: When a user starts a mobile app and when an issue or crash happens, the app may log events. This data, when combined across all mobile devices on which the app is installed, can reveal useful information about usage, metrics, and code quality.
3) Data Ingestion Google Cloud: Ingesting Bulk Data
Bulk data is made up of enormous datasets that require a lot of aggregate bandwidth between a few sources and the target. The data could be saved in a relational or NoSQL database, or in files like CSV, JSON, Avro, or Parquet. On-premises or on other cloud platforms, the source data may be found.
For performing Data Ingestion Google Cloud by ingesting bulk data, you can consider the following examples:
- Scientific workloads: Genetics data is uploaded to Google Cloud Storage in Variant Call Format (VCF) text files for further import into Genomics.
- Migrating to the cloud: Using Informatica, you can move data from an on-premises Oracle database to a fully managed Cloud SQL database.
- Data backup: Using Cloud Storage Transfer Service, you can replicate data stored in an AWS bucket to Cloud Storage.
- Importing legacy data: You can copy ten years worth of website log data into Google BigQuery for trend analysis over time.
Conclusion
In this article, you have learned about Data Validation. This article also provided information on Google Cloud, its key features, Data Lifecycle, its components, Data Ingestion and its types, Data Ingestion in Google Cloud and its types.
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Manisha Jena is a data analyst with over three years of experience in the data industry and is well-versed with advanced data tools such as Snowflake, Looker Studio, and Google BigQuery. She is an alumna of NIT Rourkela and excels in extracting critical insights from complex databases and enhancing data visualization through comprehensive dashboards. Manisha has authored over a hundred articles on diverse topics related to data engineering, and loves breaking down complex topics to help data practitioners solve their doubts related to data engineering.
