Google Big Data as a service from IT Svit
As one of the leading cloud service providers, Google Cloud Platform delivers a wide range of services, covering all the needs of modern business. Google Big Data services are various and very powerful, allowing you to leverage data processing as a part of your customer-facing systems or internal mission-critical infrastructure and workflows. IT Svit has worked on configuration, management, and optimization of various Google Big Data solutions and we can do this for your business as well!
Google Cloud Business Intelligence tool is a great managed solution for every SMB or enterprise that aims to utilize all facets of its data to enable the most cost-efficient business intelligence. It can ingest millions of events per second from any cloud-based or on-prem data sources, perform instant data validation using data processing through Google cloud Big Data tools like Cloud Dataflow, Cloud Data Fusion, Big Query data transfer API, Cloud Dataproc and various third-party modules of your choice.
The next step is building an internal data warehouse using Google Big Query, Google Data Catalog and Google DataPrem features to catalog, warehouse the semi-structured data and prepare it for ad-hoc data analysis and visualization with Google BigQuery BI engine. These results can be delivered in the form of Google spreadsheets, through Big Query SQL interface, Data Studio or various third-party systems via API. This process enables real-time operational reporting, data collaboration and advanced analytics, enabling you to deliver unparalleled value for your customers and leverage self-healing infrastructure for your systems.
Google Big Query is a powerful feature for real-time data validation, cataloging, transformation, streaming and batch processing, which is the core of Google cloud data solutions. It can ingest literally an unlimited number of real-time events through the App engine authentication server and further process them using asynchronous messaging features from Cloud Pub/Sub, or work with batches of events delivered through Cloud Storage. Later, these two streams are combined for parallel processing with Cloud Dataflow, and the output is delivered by Big Query analytics engine to visualization instruments of your choice, like Data Studio, Google Spreadsheets, Tableau, Cloud Data Lab or other BI tools.
Google Cloud also provides a wide variety of approaches for mapping various on-prem Hadoop workflows to cloud-based Google Big Data solutions. For example, if your project involves working with NoSQL workflows and Apache Accumulo, Cloud Dataproc is the right choice. If you use Apache HBase for this task and don’t need coprocessors, Google suggests Cloud Bigtable. If you need processing for streaming data and work with Apache Beam — Cloud Dataflow is your choice, but if you work with Apache Kafka or Spark — Cloud Dataproc would be the best bet.
If your business workflows include batch data processing or ELT/ETL pipelines, Cloud Dataproc or Cloud Composer will help you deal with the output of MapReduce, Hive, Oozie or Spark. If you do ad-hoc querying with Apache Spark, Hive, Drill, Impala or Presto — Google Big Query provides serverless computing for these tools, or you can configure Google Dataproc to do it.
Google provides a specific cloud service for data integration — Cloud Fusion, which is a serverless solution with a GUI and a huge library of pre-configured connectors for transforming various types of data, so that you spend less time configuring the system and more time gaining value from data processing. Build ETL/ELT pipelines with visual redactor instead of code, connect them to the public cloud, hybrid cloud or multi-cloud systems and harness all the wealth of information your cloud infrastructure can generate to get useful business intelligence and insights.
While Apache Spark and Hadoop are crucial for building efficient Big Data analytics systems, configuring them correctly requires lots of time and/or understanding your specific system parameters. GCP offers a simpler solution — Cloud Dataproc, a managed web service for running Spark and Hadoop clusters with PAYG billing and ample integration options, both with other Google Big Data solutions and third-party tools via API. With fast and highly-scalable data processing, cost-efficient pricing and huge ecosystem of supported tools, Cloud Dataproc is a valuable asset to use in your Big Data analytics.
Google Cloud AI solutions is a very functional feature with a wide variety of Machine Learning models that cater to various needs of the business:
- self-service chatbots for your customer support centers
- natural language processing tools to extract the compressed value out of verbose reports and other documents
- talent acquisition and recruitment AI, which helps the companies identify the best candidates for open positions using CV parsing and skill matching
- personalized product Recommendations AI to analyze customer interactions in real-time to adjust the purchase journeys and increase average customer engagement, order sizes and conversion rates.
- much, much more ML models that can be configured to meet your unique product requirements and help your business gain a competitive edge on the market.
Thus said, while most of Google cloud Big Data services are managed, meaning you don’t need to configure each server, cluster and pipeline individually, you still need an in-depth knowledge of data processing best practices and cloud infrastructure operations to ensure cost-efficiency of your cloud-based Big Data workflows.
While some companies — mostly Managed Services Providers like IT Svit — have this expertise at hand, many more businesses try to obtain it doing real-life Big Data projects using Google Cloud Platform services. This is a bold move, which seldom works unless you have a thorough GCP and Big Data expertise indeed. Therefore, most of the startups prefer to hire IT outsourcing companies, while most SMBs partner with GCP to get these things done.
We at IT Svit know for sure that going full-ham on managed cloud services is not the best decision. Yes, your requests will be processed by industry-leading professionals under SLA and you can rest assured your projects are safe and sound. However, GCP software engineers will use GCP web services and features to build, configure and monitor your cloud infrastructure and data flows. On one hand, these tools were built to form a seamless and cohesive ecosystem, so they will perform excellently. On the other hand, each and every one of these components cost money, so TCO for such systems can be quite high, not to mention such systems are an example of vendor lock-in, so you would have to rebuild them from scratch in case you ever move to another platform.
Thus said, the optimal choice for a business that does not have a DevOps and Big Data expertise at hand is hiring such talents from an IT outsourcing company like IT Svit. We work with a wide variety of projects from various market niches and have ample hands-on experience with building cloud infrastructure and configuration of Google Big Data solutions. But most importantly — we replace vendor-specific infrastructure components with open-source free-to-use alternatives wherever possible. This results in modular and flexible cloud-agnostic solutions for Big Data analytics, that can be quite easily reconfigured to use AWS or Azure Big Data tools and ML algorithms instead of GCP features.
IT Svit is glad to assist with configuring and managing your Google Big Data tools. Contact us today!
