Gold is a metal that has attracted humans across generations and centuries. We are impressed by its shine and rarity. Gold has other properties that make it unique. It is resistant to most acids and corrosion. It is highly malleable and ductile and has many uses
such as in making jewelry, electronics, and medicine. All this makes gold a very attractive metal.
When you buy gold, you want to be sure it is pure, and not an alloy which looks like gold. You want the real thing. And that is what you are ready to pay for too, because you know you are getting what will give you, the customer, the product you expect.
All companies want a golden data analytics platform – a platform that uses their data to the fullest potential and helps make business decisions that enable higher revenue and profit. But instead of looking at the real properties of the platform, they are at many times impressed by its shine and look – that is the user interface, and newness. There are several new data analytic platforms, data transformation and consumption services available today, especially in the cloud. Having a wide choice of platforms and services is good for companies, but those who end up just focusing on the shine end up with massive disappointment, much like getting adulterated gold.
Cloud services – be it data analytic platforms, data transformation and movement tools, and/or data analysis tools--- are available in many options now. Some are “cloud native” which means they were developed to use cloud infrastructure properties like serverless computing, resource (CPU, storage, memory) scaling, separate storage and compute, etc. Others like Teradata Vantage, which have been around for decades, have adapted to the cloud by applying cloud properties to their services, while retaining their original strengths.
Nevertheless, the choice of such services is indeed overwhelming and decision fatigue is bound to happen. But it is wise to go through the pain of understanding the real properties of the choices you have before you make a decision. Companies moving from on-premises to cloud often are given a “modernization” promise by native cloud services, but customers do not look at the real properties of these services – do they have all the features I currently have on-premises? Can they handle the data volume, velocity, and variety that I need? Can they handle the high query concurrency, and user concurrency? Can they handle workloads with a mix of short, medium and long running workloads with varying use of compute, memory, storage, and network resources? Can ETL tools do all the types of transformations I need? Can they connect natively to various data sources and targets? Are BI and analytics tools easy for use by business users and data scientists? Do I get the same or better performance to meet my SLAs? Will these services meet the needs of my business and architecture roadmap? Will I just be moving from one platform/application to another spending months/years and money to move, and end up getting lesser value? All these are important properties to consider.
Pure gold is extracted by smelting its ore under high temperature and pressure in a furnace. Teradata has spent decades in enhancing and optimizing its Teradata Vantage
data analytics platform by working meticulously with hundreds of large corporations across the globe, and as a result of that, became the gold standard for a modern Data Analytic Platform in the cloud
, or on-premises. It gives you all the properties you need for a truly integrated data management platform, allowing descriptive, predictive, and prescriptive analytics, across multi-cloud environments to serve numerous applications in your ecosystem. Focus on the right properties … then on the shine.
Rahul Shiyekar is the Practice Head for Architecture & Design at the Global Delivery Center, Teradata. Rahul has 25 plus years of IT experience. He has been with Teradata since 2007 and was a Teradata customer for three years prior to that. At Teradata, Rahul has started, led and grown high performance teams in areas of Solution Architecture, Competitive Benchmarks and Performance & Workload Management. He has played the roles of a Data Warehouse Architect, Product Manager, Data Modeler and Software Developer in previous organizations in the USA. Rahul has a Masters of Science degree from The University of Texas at Austin.
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