Flexera’s 2023 State of the Cloud Report reveals that 45% of organizations are spending more on the cloud amid economic uncertainty. This bodes well when we consider the numerous advantages that enterprises can enjoy with cloud usage, including cost optimization, better reporting, enhanced scalability and flexibility, automated policy governance, etc.
Such advantages pave the way for moving substantial workloads to the cloud, including the data analytics function. Here we explain how the cloud can become the cornerstone of your data analytics strategy, along with the challenges it resolves.
Challenges or Obstacles to Scaling Data Analytics
Organizing the Big Data
IT managers and other business users are frequently overwhelmed by the volume of data generated in today’s data-driven organizations. Every interaction across every touchpoint is worth considering; however, managing all this data leaves analysts with numerous interlocking data sets, which can be tough to navigate.
Planning the Budget
Data analytics involves increased efforts in budget planning as enterprises have to address the large volumes of data and their storage, processing, and analysis. Along with this, costs are incurred while refining the data quality as well as integrating it.
Organizations must guarantee that their infrastructure can meet the increasing needs of data storage, processing, and analysis. To accommodate larger datasets and sustain efficient data analytics operations, infrastructure scaling entails increasing storage capacity, increasing computational power, and optimizing data processing pipelines. The deployment of large-scale data analytics functions can lead to high IT resource consumption.
Moving Data Analytics to the Cloud
Easy Tracking of Products & Preferences
Organizations may learn more about user preferences across many platforms by consolidating client data in the cloud. They can integrate data from several channels and data centers.
In reality, cloud technology makes it possible to divide up user data into many categories depending on facets like behavior, preferences, and interests. This aids in effectively tracking customer preferences. Enterprises can then easily perform more advanced functions, such as identifying the sentiment (positive, negative, or neutral) expressed by users towards particular goods, services, or brands, giving us important information about their preferences and levels of satisfaction.
Much broader functions can also be seamlessly accommodated. For example, businesses can detect high-impact material, current topics, or developing concerns that demand rapid action or response by evaluating social media activity across numerous channels.
Smooth Recording and Processing of Data
Cloud analytics enables the simultaneous recording and processing of data despite the location factor of local servers. The concurrent processing of data across several nodes is made possible by distributed computing frameworks like Apache Hadoop and Apache Spark, which are supported by cloud platforms. By distributing the task over a cluster of workstations, organizations are able to analyze enormous data sets more quickly and effectively.
As such, companies can track an item’s sales across all of their locations or franchisees in any country and change production and shipments as needed. This way, businesses have a simple means of adjusting their inventory and adjusting their production and shipping procedures. Moreover, they can create a clear view of operational and financial data, which can be regularly analyzed to gauge if the business is performing as it should.
Optimizing Costs & Enhancing Scalability with the Pay-As-You-Go Models
The pay-as-you-go cloud model means you only pay for the services you utilize. This can help organizations avoid over-purchasing of assets and services they do not need, something that plagues operations when analytics is handled on-premise. That’s because on-premises infrastructure entails significant upfront investments in hardware, software, and the requisite maintenance, regardless of whether the actual consumption would
Businesses can scale the usage and provisioning of resources during times of high demand. Likewise, they can minimize the same during off-peak periods, guaranteeing they only pay for what they need. Then there’s the advantage of money being saved from the elimination of infrastructure upkeep and management. After all, cloud service providers manage infrastructure updates, privacy, and maintenance activities. Enterprises don’t have to fret about that.
Implementing Cloud-based Data Lakes and Data Warehouses
How Do Data Warehouses Help?
Choosing between a data warehouse and a data lake, or zeroing in on the requirement for intricate connections between the two, becomes less important when organizations migrate their data architecture to the cloud. Organizations increasingly have both and utilize flexible data movement from lakes to warehouses to support business analysis.
A solution like Snowflake is a great example to address the growing needs of organizations, with its excellent efficiency, high concurrency, ease of use, and cost-effectiveness. For instance, Snowflake:
- Keeps the data well-governed and queryable
- Provides unlimited storage at a reasonable cost
- Provides the functions of business intelligence and advanced analytics. It features a profound partner ecosystem by allowing integration with tools like Tableau, Looker, Sigma, Talend, Alteryx, etc.
- Has a tight architecture for data integration
- Eliminates administration hassles and offers high scalability
How Does a Data Lakehouse Help?
The data lakehouse method is a modern data architecture that brings together the concepts of the data lake, data warehouse, and any other purpose-built services into a seamless whole. With the use of data lakehouses, unstructured data of the kind that is generally kept in a data lake can be given a structure and schema similar to those used in a data warehouse.
Databricks Lakehouse Platform is an excellent example in this regard, as it serves to combine the ACID (Atomicity, Consistency, Isolation, and Durability) transactions and data governance of enterprise data warehouses with the adaptability and cost-effectiveness of data Lakes. Its benefits to the organization are as follows:
- It’s an excellent amalgamation of a data warehouse and data lake, providing combined features.
- It also can process the data with Spark, adding to its utility.
- It covers a broad range of events like reporting and streaming.
- It is even effective in cases of downtime with excellent stability.
- On top of everything, it provides excellent and prompt customer service.
- It’s priced reasonably and is an all-in-one solution.
How Can an Expert Technology Partner Like Pratiti Tech Help
The cloud offers clear advantages over on-premises data analytics, and its adoption is growing exponentially. But, as with all technologies, it’s vital for businesses to explore what it can do for them, what it can’t, and how to get the most out of it.
At Pratiti Tech, we have an expert team with decades of experience in building scalable and efficient cloud-based solutions. We help businesses:
- Seamlessly migrate data workloads to the cloud without data duplication
- Centralize and streamline the cloud data analytics and reporting efforts
- Reduce the TCO by fine-tuning data analytics workloads
- Democratize access to insights for empowering business users to contribute to important decisions
- Measure and evaluate the performance of the data analytics function. After all, you can’t manage what you can’t measure.
Ready to set up a cloud-based data analysis solution? Get in touch with us today!