What is Google Analytics?
Google Analytics is an Analytical Platform where organisations can play with their data and analyse it on any scale and cost-effectively. Google Analytics can measure data from many dimensions and in a way that organisations specify. Orchestration is based on Cloud Composer, which is built on an open-source tool, Apache Airflow. Thus, it provides a more cost-effective service for orchestrations. Directed Acyclic Graphs can also be viewed and managed to optimise the Big Data processing pipeline.
What are the capabilities of Google Analytics?
The main capabilities of Google Analytics are below.
Google Data Lake
Residing data on the cloud and using it when required to process data for analytical and query purposes is the objective every organisation seeks. Google Cloud is the storage where one can store any data (i.e., Parquet, AVRO or CSV) in its raw form, and later it can be accessed by various Google Analytics Tools such as Datalab, Dataprep and so on for different tasks. Data Lakes are best suited for storing aggregate data, and Batch ETL are easy to perform as data is already present on the cloud. Store Data in a Data lake by streaming pipelines for more valuable insights.
How much data can we store on Google Cloud Storage?
We can store exabytes of data on Google Cloud Storage (which they call a Data lake). Each file stored on Google Cloud Storage can be 5TB, and each object can be a maximum of 5MB. For objects exceeding 5MB, they can use multipart to store them on the cloud.
What if Someone needs to store data for a few reads?
Google Analytics has defined four storage classes with different purposes. One can choose according to their requirements. If one needs to access data once a month or once a year, they can use near-line storage buckets or Coldline storage buckets. The price of these storage classes is a little bit higher than the others.
Google Stream Analytics
It is a fully managed infrastructure for managing real-time data processing and an analytical pipeline. Google Pub/Sub can be used for ingesting data from many streaming sources such as IoT sensors and then Dataflow with Beam can be used to apply certain transformations on data. This transformed data then can be more accurately filtered with fully.managed data warehouse service, BigQuery. One can run SQL queries using BigQuery and have the data available for Analytic.
Google BigQuery
BigQuery is a data warehouse solution by Google on Google Cloud Platform. BigQuery Applications are helpful in analytics of large and complex data sets to process some business logic or client application software requirements building. One can collect data from object storages or cloud store by creating a data warehouse for analyzing batch or stream data by using BigQuery. It is easy to load data into BigQuery by using Cloud Dataproc or Cloud Dataflow with Apache Beam for ETL. Once data is in BigQuery, we can run SQL queries on it to generate a specific type of data for Analytics.
Cloud Pub/Sub
Cloud Pub/Sub is a service for ingesting streaming analytics pipeline data. In Pub/Sub, publisher Applications publish the messages that Cloud Pub/Sub receives and handle them by writing to Subscribers. These publishers can be Storages or Analytical Services such as BigQuery, DataFlow, etc. On the Google Analytics Platform. Using Pub/Sub alongside Dataflow data access with Warehousing tools BigQuery and BigTables.
Cloud Dataflow
Cloud DataFlow is a serverless approach that removes the overhead of managing scale, flexibility and other related parameters. When a need to process data with complex Aggregations, Windowing and complex filtering, Cloud Dataflow play the critical role on Google Analytics platform. The code of Apache Beam is purely based on Cloud Dataflow. So, one can use Beam for data transformation related to pipelines. Once data is collected by Dataflow one can use BigQuery for data warehousing and then apply simple SQL queries on data for extended application roles such as Analytic, data management and so on.
Cloud Dataproc
Cloud dataproc is a tool for batch processing data pipelines. When there is a need to process data Lake data based on Hadoop and by using data processing frameworks such as Spark, Cloud DataProc can be used. One can use Spark to process Hadoop System-based data files stored in Google Cloud Storage (data lake). We can perform general Batch ETL and complex Batch ETL on dataproc. For analytic use SQL on hive or Spark SQL. Also, can use Spark ML for machine learning operations.
Building stack makes it easier to work with components as it brings modularity, increasing composability. Source: Analytics Stack on Google Cloud Platform
Cloud ML engine
For analytical purposes related to machine learning, Cloud ML engine has the flexibility to train the dataset to build a model. ML engine is based on Tensorflow. Establish a model once, and then there are two options for predictions. One is Online prediction for Serverless management of AI models or ML models, and other is a Batch prediction for cost-effective asynchronous applications. Tensorflow SDK is available to utilize all the functionalities of ML engine properly. Dataprep can be used for intelligent exploration, cleaning, and preparing data for Analysis and ML. Serverless ML APIS are available for BigQuery and Cloud Storage user applications.
Cloud Big Table
Cloud Big Table is a NoSQL store based on Apache HBase. One can store streaming data or transformed data into NoSQL format in Big Table. Data visualisation using BigQuery, DataFlow, and DataProc will make it easy to analyse. BigTable has the capability of database and cache-related operations.
How is Google Analytics better than other solutions?
Google Analytics provides more robust, Serverless and fast cluster loading environment for big data processing and Analytic. BigQuery, BigTable, DataProc and DataPrep like solutions offers more flexibility to work on open source tools that will reduce cost and serverless architecture provides more robustness and removes cluster-related issues. Develop Data Pipelines for Batch and Real-time applications differently or under one hood.
The data lake has no limitations on storing data and supports all types of raw data formats. It has some visualisation tools that represent the data from different dimensions. For Example, Organizations can check how many clicks they get and where. Applying transformations in such a way can help organisations build a more hybrid but easy-to-use model for decision-making and big data processing. Moreover, the ML engine reduces the time to train a model and use it. There are several pre-processed and trained Serverless ML APIS available that can help to start and run the application without any delay.
A Data Analytics Approach
An Analytics-Driven Approach assists you in gaining extensive knowledge of your consumers so you can deliver better experiences and drive results. To grasp more about analytics, we encourage talking to our experts.
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