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The Apps Admin Blog Google Cloud

Big Data with Google's Big Caddie

  • August 2, 2018

Copy of apps admin blog (6)It doesn't matter whether you operate in the world of finance, health, or even retail, the chances are that you've already heard of "Big Data". The concept of Big Data refers to any information that would otherwise be too expensive to manage, store, and analyze using traditional monolithic or relational database systems. These systems are frequently inefficient because they're inflexible when it comes to storing unstructured information like text, images, and video.

In recent years, as new technology continues to capture broader sets of data, the mainstream marketplace has begun to adopt new approaches for processing and managing big data. This is particularly important for companies who connect with the "Internet of Things" - the system that includes a global network of interconnected sensors and devices.

While huge amounts of data might be difficult to organize and store, for companies who can process this information correctly, the advantages can be significant. From retail to financial services, in every sector big data allows companies to understand more about how their companies work, and what their customers want.

Cloud computing solutions like the GCP ensure that companies can gain sustainable access to the processing, storage, and analytical aspects of big data on a secure and cost-effective basis. This is crucial for customers who are seeing their data volumes growing exponentially. What's more, the GCP also offers additional ways to experiment with the data collected, through Machine Learning, for instance.

The Google Cloud Platform and Big Data

Though it might advertise itself differently, at its core, Google is simply a mountain of information and a broad collection of tools that companies can use to work with that data. Over the years, Google has evolved from a simple index of web pages to a central hub for real-time data on anything that might be able to be measured.

Big data analytics, which involves using tools to sort through and understand the data collected, is something that happens every time someone carries out a Google search. Google runs complicated algorithms to make sure its offering information that matches the query you enter. For more complicated processes, Google involves other inbuilt algorithms that are also based on Big Data - such as translation AIs and more. In the future, Google may even invest in blockchain to ensure that all of the information gathered and shared remains secure.

From the largest business to solo entrepreneurs, everyone can benefit from making use of big data analytics. The more you learn about your industry and the people that buy from you, the more you can adjust their buyer journey to suit their needs and your desire for profit. Google offers an end-to-end big data solution that comes from their own innovative tactics used to power the Google search engine.

With the Google Cloud Platform, you can discover real insights into your company and your audience.

Google Cloud BigQuery

At the heart of Google's Big Data strategy, is Google "BigQuery". This is a serverless, scalable, and low-cost data warehouse intended to make data analytics easier. Because there's no need to manage any infrastructure, companies can focus entirely on finding the insights that are most meaningful to them, using a language that they understand.

BigQuery allows organizations to analyze their data in depth by creating a warehouse of information. It also ensures that it's easy to share insights securely with coworkers and partners through spreadsheets, datasets, queries, and reports. You can even capture and assess information in real-time, using powerful streaming services, so your insights are always current. Features include:

  • Real-time analytics: Big Query's high-speed streaming API ensures that you can access all the benefits of real-time analytics. You can evaluate what's happening by making your business data ready for analysis instantly.

  • Logical data warehousing and federated query: BigQuery breaks your data into segments so you can analyze all your assets in one place. Through a powerful federated query solution, all your data can be processed within Google Cloud Storage or transactional databases like Cloud Bigtable.

  • Automated backup and easy restore: BigQuery makes it easy to replicate information and keeps a history of the changes you make, so you don't have to worry about losing crucial information.

  • Data transfer: If you're just getting started with data warehousing then BigQuery can help. Even if your data is held up in a SaaS application, the BigQuery data transfer solution automatically moves your information from external sources like AdWords or YouTube into BigQuery.

  • Ecosystem Integration: With the support of Cloud Dataflow and Cloud DataProc, BigQuery ensures that you have easy integration with the Apache ecosystem, which means that your existing Spark and Hadoop workloads can take information straight from BigQuery. Essentially, the tool allows you to make the most of your data by making it easier to analyze and integrate into your existing Big Data strategies.

  • Data governance: BigQuery is equipped with the fine-grained access controls and role-based control on APIs through integration with Google Cloud IAM. With Cloud IAM and Big Query, companies can rest assured that their information is secure.

  • Security and encryption: You'll have complete control over who should be given access to the data you store in your Google system. With BigQuery, it's incredibly easy to manage your data, so you can keep everything secure.

Google Cloud Dataflow

The Google Cloud Dataflow solution is a fully-managed GCP service for enriching and transforming the data you collect in real-time and historical modes. The tool ensures that making the most of your data is easy with reliability and high performance. You won't have to worry about conducting complicated workarounds or compromises, and you'll have complete access to an almost limitless range of tools to solve your big data processing challenges. Features include:

  • Automatic resource management: Dataflow automates the management and provisioning of processing resources to maximize performance and minimize latency, so you don't have to spin up any instances by hand.

  • Dynamic rebalancing: To make sure that your entire network is constantly performing at its best, Google Cloud Dataflow automatically rebalances lagging work, so you don't have to pre-process input data or find hotkeys.

  • Reliable processing: Dataflow comes with in-built support for fault-tolerant data input, so you can continue to get results regardless of processing pattern, data size, cluster size, or the complexity of your pipeline.

  • Horizontal scaling: With Horizontal auto-scaling of resources, you can get better results with reduced price to performance.

Google Cloud DataProc

Simple, effective, and ready to use, Google Cloud Dataproc is another fantastic managed-cloud service which helps companies to make the most of their Apache Hadoop and Spark clusters. The GCP makes managing and using these clusters more efficient, ensuring that operations that might have once taken hours or days take a matter of seconds to complete.

The innovative billing system of DataProc also ensures that you only pay for the resources you use within the system with per-second billing. Additionally, Dataproc is also designed to integrate with the rest of the Google Cloud Platform, so you can access a complete platform for analytics, data processing, and machine learning. Features include:

  • Automatically manage clusters: Google automates the processes of deployment, monitoring, and logging, so you can keep your focus on your data, rather than your clusters.

  • Scalable clusters: The data clusters you create can be scaled fast with a range of different virtual machine types, node numbers, disk sizes and options for networking.

  • Integration: The Google Cloud Dataproc system integrates with the rest of the GCP, including Cloud Storage, Bigtable, BigQuery, Stackdrive monitoring, and logging, so you have a complete platform for your data.

  • Versioning: Worried about losing data? Image versioning ensures that you can switch between different versions of Hadoop, Spark, and other tools. You can even run clusters with a range of master nodes to make sure that your jobs are as simple and accessible as possible.

  • Developer tools and support: Enjoy multiple ways of managing clusters, including the Google Cloud SDK, Web UIs, and REST APIs. You can also access manual or automatic configuration depends on what you prefer for your company. Run simply initialization actions to customize the libraries and settings you need when you create your clusters.

Google Cloud Datalab

Google Cloud Datalab is an exciting and powerful tool that helps companies to transform and explore data and build their own machine learning models through the Google Cloud Platform. The entire system runs on the Google Compute Engine, which means that it connects to multiple different cloud services at once.

Cloud Datalab makes it easy to process your data with solutions like the Cloud Machine Learning Engine, Cloud BigQuery, and Cloud Storage. Things like cloud computation and authentication can be managed out of the box. Features include:

  • Multiple languages: Cloud Datalab supports JavaScript, Python, and SQL, so you can choose the language that you feel most comfortable with.

  • Notebook: Datalab can combine documentation, codes, results, and visualizations into a unique notebook format so you can manage your data more efficiently. There's also the option to use Google MatPlotLib or Charting to create visualizations.

  • Machine learning opportunities: The Datalab configuration supports the machine learning models offered by Tensorflow, so you can begin to develop your own AI ready solutions on the Google Cloud.

  • Pay per Use Pricing: You only need to pay for the resources you use when it comes to BigQuery, Google VMs, and any other additional resources within the data portfolio. This ensures that data management and analysis is a possibility for any shape or size of business.

Google Cloud Dataprep

One of the more recent developments in the Google Cloud Platform, Dataprep is an intelligent and innovative data service that allows users to explore, manage, and prepare unstructured or structured data for analysis. The Dataprep solution, like many of the Google Cloud services, is serverless and ready to work at any scale. There's no need to deploy and manage your own infrastructure, which means that prepping data has never been simpler. Features include:

  • Quick insights into data: Within seconds, you can interact and explore the information you're gathering within your business. Dataprep makes it easier to understand data patterns and distribution, and there's no need to write any code to get started.

  • Data cleaning: The Cloud Dataprep solution automatically looks for anomalies within your data portfolio and helps you to correct the problems quickly. You can get suggestions for data transformation based on usage patterns, structure, standardize, and manage datasets with an easy and guided approach.

  • Powerful: The Cloud Dataprep solution is designed to work on top of the pre-existing Google Cloud Dataflow system. Dataprep is easy to scale and it can make it easier to process huge amounts of data. Because it integrates with the rest of the Google Platform, users can process their data whether it's stored in the Google Cloud, BigQuery, or a desktop environment. You can also export clean information to BigQuery for additional analysis.

  • Versatile support: Dataprep supports the management and cleaning of data of any shape or size. Users can process diverse sets of data, whether it's structured or unstructured, and transform the information in JSON, CSV, and other table formats.

Google Cloud Pub/Sub

Google Cloud Pub/Sub is a reliable, effective, and easy-to-scale solution for event-driven computing strategies, and real-time analytics. Pub/Sub comes as part of the Google Cloud stream analytics portfolio, and it's designed to manage event streams and deliver them seamlessly to Cloud Dataflow for processing, or Google BigQuery for analysis as a warehousing solution for data. Relying on the Cloud Pub/Sub service for the delivery of data frees you up to focus on transforming business systems easily and effectively. Features include:

  • At-least-once delivery and exactly once processing: Cross-zone and synchronous message replication and pre-message receipt ensures at-least-once delivery for any scale of data management. Additionally, the Dataflow system supports the expressive, reliable, and first-time processing of Pub/Sub streams.

  • Automatic everything: There's no need to provision anything with Cloud Pub/Sub, as there are no partitions or shards, you can simply set your quota and get to work.

  • Open and Integrated: Just like many of the solutions on the Google Cloud platform, Cloud Pub/Sub integrates with multiple services throughout the Google portfolio, including Cloud Storage. The open APIs and client libraries are available in seven different languages to support a range of hybrid and cross-cloud deployments too - making accessibility easier.

  • Security and compliance: Pub/Sub is a highly compliant service and can even be suitable for those who need to contend with HIPAA guidelines. It offers absolute end-to-end encryption and access controls.

Google Cloud Genomics

Genomics is a service within the Google Cloud data platform that's specifically designed to support the life sciences community with big data and machine learning. This tool makes it easy to access and use important information with extensions to the Google Cloud platform that allows users to apply the same tech that powers Google Maps and Search to securely process and explore complicated datasets. Features of Google Cloud Genomics include:

  • Interoperability and Integration: Google implements the open standard taken from the Global Alliance for Health and Genomics to ensure that their system is interoperable across various genome repositories. The solution is also backed by effective Google tech like Spanner and Bigtable. Additionally, as with many other aspects of the Cloud Platform, the Genomics service is integrated with databases like Datastore and Bigtable.

  • Compliance and security: The system is covered by the HIPAA Business Associates contract, which means that you can rest assured that you're complying with the latest regulations in the life sciences industry.

  • Real-time data processing: The Genomics system offers real-time analysis and data processing with tools like Cloud Datalab and BigQuery. This means that you can begin to access and manage data from the second you get it.

  • Highly scalable: You can load huge amounts of data onto Genomics with annotations, references, variants, and more.

Google Cloud Data Studio

Finally, we come to another exciting recent development within the Google Portfolio. Google's new "Data Studio" offering is designed to transform your data into an informative dashboard or report of information that's easy to share, read, and use. The dashboarding strategy ensures that you can access huge amounts of information easily, to support high-quality business decisions.

GCP users can connect their data to reports from Google Cloud SQL, Google BigQuery, and more. You can also connect data from Google Analytics, Sheets, AdWords and YouTube channels. Once you're finished collecting your data, you can create metrics, dimensions, and calculations to transform your data without having to update initial sets of raw data. Features include:

  • Data visualization: Choose from an array of graphs, charts, and visualizations to bring your information to your teams in an easier-to-consume format. Options include bar charts, time series, pie charts, tables, geo maps, heat maps, scatter charts, and more. Every visualization comes with its own in-built comparison functions.

  • Report management: With Google data studio, you can transform and manage every aspect of your dashboards and reports to make them your own. Add icons and logos to the mix, change your background, and text colors and choose from an array of different styles to make your reports as effective as possible.

  • Collaboration and sharing: Data Studio is designed using the same tech that runs through the remainder of the Google Cloud Platform, including the high-performance GSuite products like Slides and Docs. This means that you can make collaboration and sharing easier than ever, deciding who should be given access to your reports with a couple of clicks. Multiple people can even collaborate on a document at the same time.

  • Report Templates: There's a huge collection of report templates to choose from, which ensures that you can be up and running within a matter of minutes. Simply choose your data sources and then customize your design to suit your needs.

Is It Time to Make the Most of Big Data?

As the sheer volume of data in the business world continues to grow, it's crucial for companies to understand just how valuable this information can be. With the right data solutions from the Google Cloud Platform, companies can transform their raw information into predictions, relevant trends, and projections for the future. Companies that use comprehensive solutions for Big Data analytics will reap the benefits, gaining more insights to drive intelligent decision making. Just some of the advantages of big data include:

  • Efficiency improvements: The rapid performance of tools like Hadoop ensure that you can easily identify and access new sources of data, which makes it easier for companies to analyze situations and make important business decisions.

  • Cost savings: Some Big Data tools can bring significant cost savings to organizations because it can help them to understand where the inefficiencies in their company exist. This makes it easier to optimize performance.

  • Product development: Understanding customer needs and trends makes it easier to create products that suit the needs of your customers. You can also analyze big data to get a better understanding of the marketplace you're working within, and how you can get ahead of the competition.

  • Machine learning: Big data is fundamental to the development of machine learning strategies and artificial intelligence. Those considering investing in virtual assistants and chatbots will need big data.

Whether you're trying to improve the efficiency and performance of your business with a better understanding of your market, or you're interested in concepts like machine learning, Big data is something that can't be overlooked. While Big Data is complicated on the surface, tools like the Google Cloud Platform ensure that any kind of business from any environment can make the most of the information they gather.

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