Introduction to Analytics Stack on GCP
Before moving forward to building an Analytics Stack on Google Cloud Platform, let's take a look at what a stack is. Many of us might confuse it with the Data Structure stack, but that is not the case here. A stack can be referred to as a collection of technologies. As technologies and the cloud evolve every second, we can integrate technologies/applications into our software/solution stack. Every business is refining its process by incorporating refined software stacks.
Exploring productized analytics solutions is important because companies rely on analytics to understand big data and compete in today’s data-driven landscape. Source: Leveraging Stacks
Building a stack makes it easier to work with components, as it brings modularity, increasing composability. Only with the oldest trick in the playbook, divide and conquer, can we break down complex components into simpler pieces that can be enhanced by adding other technologies, just like the data structure stack!
What is Analytics Stack?
At the most rudimentary level, it is the bridge between raw data. It is the combination of coherent applications that combine and probe to realize the value of data. Let us look at an analogy: water data is necessary,y and pipelines bring it to your reservoir. As a building needs good plumbing, an organization that envisions becoming data-driven and wants to tap into this unextracted wealth must maintain it well and have a competitive edge.
As the most profitable businesses continue to set new benchmarks for productivity and innovation, their rivals must adopt analytics to stay competitive regardless of scale. Fortunately, the elements of an analytics stack are getting easier to set up, maintain, and scale at a lower cost.
What is a Data Warehouse?
With the amount of data flowing and the idea of stack adding on the weight of maintaining it, it becomes crucial for any company that wants to extract and get real value from their data. However, after working out solutions from existing resources, companies head towards a roadblock where they discover they lack the infrastructure to use their data fruitfully. They might not have the skill set required to analyze the information and change with it effectively. Every module, every component of it, requires a unique skill set. While there are many big sharks in the tank, let us talk about Google to build an analytics stack.
Enable Real-Time Stream Ingestion, storage and processing to drive game-changing, enterprise-level significant improvements. Click to explore our Real-Time Analytics Services and Solutions
Building Analytics Stack with GCP (Google Cloud Platform)
Ingest (app engine, pub/sub, cloud functions) While we Build an analytics stack on the GCP. We can explain components that can help under different categoriesIngest/ETL
Below are the components that help build it on the Google Cloud platform.
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Cloud Functions
Google Cloud Functions are a serverless environment that enables you to build cloud applications. Its lightweight computing solution allows you to construct stand-alone serverless applications without any overhead burden to manage the environment or servers. You write simple single-purpose applications run in an event-driven architecture with cloud functions. -
Pub/Sub
It is an asynchronous management service that enables you to build accurate event-driven applications by decoupling them from each other. It helps to ingest data at high speed and high availability in real time for streaming applications. Pub/Sub by Google generally helps with Balancing workloads in network clusters, implementing asynchronous workflows, distributing event notifications, Refreshing distributed caches, Logging to multiple systems, Data streaming from various processes or devices, and, most importantly, Reliability improvement. -
App Engine
App Engine is a container service on Google's infrastructure, with available preconfigures and several runtimes. It enables you to build and deploy load-heavy applications that can process large volumes of data. Applications run in their independent containers, enabling multi-server access that is easy to scale and has no overhead burden to manage Cloud applications. -
Dataflow
Capturing, analyzing, and real-time processing data is a tedious task. In addition, the data coming might be unstructured or semi-structured, which is difficult to process and not in the apt format required by the dependent downstream applications. GCP provides a solution for this, which is dataflow. Dataflow is a fast and cost-effective stream and batch processing service.
Dataflow helps automate the process and scale quickly with any cluster burden management. It is based on a simple source-sink architecture to transform your data. It provides modularity and Apache beam SDK that can be developed in Python and Java.
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Dataprep
Analysts and data scientists often find that the data provided is not ready for immediate use and spend most of their time cleaning it. This is where data prep comes into play by escalating the process and making the business more responsive and data-driven. It is a visual data cleaning service that can visualize, explore, clean, and prepare data for further use.
Modern DW requires Petabytes of storage and more optimized techniques to run complex analytic queries. Click to explore about our, Modern Data Warehouse Architecture
Data Warehouse
Big Query helps the data warehouse by building it on a Google Cloud platform.
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Big Query
While we build a stack, the most crucial key feature is data. Storing and querying this data might be time-consuming. The Big Query is Google's data warehouse that solves the problem by incorporating fast SQL queries with the enterprise's reliable infrastructure. - Dashboarding
Data Studio helps build a dashboard on the analytics stack on GCP. -
Data Studio
Google is a free tool that helps visualize data easily, informatively, and shareably in the form of customizable dashboards. It allows connecting to various data sources and visualization of data with highly configurable charts and tables. Data Studio lets you share informative insights with the team by speeding up the report creation process. In short, it allows you to narrate your Data through a story.
Monitoring
Stack Driver helps for monitoring the Analytics Stack.
Stack Driver
It provides robust monitoring analysis and diagnostics in the Google Cloud Platform. Stack Driver provides insights into applications' health and performance, enabling you to find and fix issues faster.
Reporting pattern detections and exhaustion predictions.
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Stack Driver Monitoring—It provides insight into longer-term trends that might require retention. It provides a single integrated service alerting dashboards, metrics, and uptime services, reducing time spent managing different systems.
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Stack Driver Logging—This provides logging services to analyze logs, generate outlines to trace issues, and quickly resolve errors, bugs, and hotfixes.
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Stack Driver Debugger - Helps see the state of running applications without affecting their performance.
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Driver Trace for Stack—This tracing system gets data from your app engine applications and displays it in NRT (near real-time).
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Driver Profiler for Stack: This tool helps you get your actual compute time and provides CPU usage that can be used to estimate pricing.
Conclusion
Hence, we can say GCP provides various technologies to build up an analytics stack on the Google Cloud platform without the overhead of managing clusters and configurations. You can build an analytics pipeline from scratch, from ingesting to ETL, to prepare and monitor data warehousing. All can be done at a place with an enterprise's reliability infrastructure.
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