This is part one of three.
When we migrated our Big Data solutions to the Google Cloud Platform (GCP), around the beginning of 2019, we were somehow worried about how the costs could vary widely depending on how we would organize it and also how our internal users would make use of the data provided, mostly in BigQuery (BQ). Also, we did not want to have our internal users worried about costs when using the data, but at the same time, we also did not want that silly mistakes could cost us more than necessary, given that we had opted for an on-demand cost model.
So this post is the story about how we approached the cost issue.
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I want to first tell you what we did, in what order and why. Also, in case we had made any mistakes, then I will share my thoughts about them and also touch on what we were thinking and what we now consider as improvements.
It is important to say, though, that no one on the team was experienced with GCP at the time, and even after more than one year using it, I’m sure we still have much more to learn.
Training internal users
Most of the cost sources would be managed directly by the team, except for what could become a big chunk of our costs, which is BigQuery. We have product analysts, data scientists, business analysts and many others that make use of the data generated by our processes. At the time of the migration, they were all used to Hive, and with the configuration that we had at the time, nothing they could do would generate additional costs. Now, the task was to both ease their transition to BQ and to instruct them on how to be now cost-aware.
#bigquery #monitoring #cost #google-cloud-platform #big-data
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The Cloud offers access to new analytics capabilities, tools, and ecosystems that can be harnessed quickly to test, pilot, and roll out new offerings.
The Cloud offers access to new analytics capabilities, tools, and ecosystems that can be harnessed quickly to test, pilot, and roll out new offerings. However, despite compelling imperatives, businesses are concerned as they move their analytics to the Cloud. Organizations are looking at service providers who can help them allocate resources and integrate business processes to boost performance, contain cost, and implement compliance across on-premise private and public cloud environments.
The most cited benefit of running analytics in the Cloud is increased agility. With computing resources and new tools available on-demand, analytics applications and infrastructure can be developed, deployed, and scaled up — or down — much more rapidly than can typically be done on-premises.
Unsurprisingly, cost reduction is seen as a significant benefit of cloud-based analytics. A complex algorithm processing large volumes of data may require thousands of CPUs and days of computing time, which can be prohibitive for companies without existing in-house compute and storage resources.
With the Cloud, organizations can rapidly access the required compute and storage power on demand and only pay for what they use. Research shows that migrating analytics to the Cloud can double an organization’s return on investment (ROI).
Standardization, cited as the third most crucial driver of migrating analytics to the Cloud, is strongly linked to the first two benefits of increased agility and reduced IT costs. Also, standardization helps organizations with simplified, streamlined IT management and shortened development cycles.
The Cloud offers access to new analytics capabilities, tools, and ecosystems that can be harnessed quickly to test, pilot, and roll out new offerings. For instance, organizations can take advantage of cloud-based data integration and preparation platforms with pre-built industry models. Leverage cloud services that offer powerful graphics processing unit (GPU)-based compute resources for complex analytics and tap into a collaborative ecosystem of data analysts within a federated data environment.
#big data #big data analytics #cloud migration #big data analytics platform #big data services #cloud analytics #big data solutions #big data analytics companies
A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.
Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.
By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.
However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.
Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.
Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.
Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.
Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.
The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.
For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.
#cloud computing services #all #hybrid cloud #cloud #multi-cloud strategy #cloud spend #multi-cloud spending #multi cloud adoption #why multi cloud #multi cloud trends #multi cloud companies #multi cloud research #multi cloud market
Big Data has played a major role in defining the expansion of businesses of all kinds as it helps the companies to understand their audience and devise their business techniques in accordance with the requirement.
The importance of ‘Data’ has been spoken very highly in the modern-day business. Thus, while using big data analysis, the companies must keep away from these minor mistakes otherwise it could have a major impact on their performances. Big Data analysis can be the silver bullet that can answer your questions and help your business to scale newer heights.
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Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.
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