![]() Microsoft swiftly adapted to the competition from a non-IT rival. However, this initial dominance did not last. At first glance, AWS seems to have an advantage in terms of longevity. ![]() ![]() Snowflake, the newest entrant, was established in 2012. Google Cloud, its notable competitor, entered the scene in April 2008, followed by Microsoft Azure in October 2008. While IBM and Oracle also offer their own solutions, we will focus on the "big four" providers for now, as their offerings operate similarly.Īmazon Web Services emerged in 2006 as a spin-off of Amazon's extensive data center infrastructure. The major players include Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and Snowflake. Let's examine the key players in the cloud data storage industry. Incompatible security & governance models – Both platforms offer different governance models that are not compatible with each other.ģ.ĝifferent data on different platforms – Data warehouse relies on BI use cases whereas data lake relies on AI use cases which stands outperforming differently.Īfter seeing all the challenges of operating on two different platforms, what if companies can do all on one platform having one security & governance model? This complexity creates 3 major challenges:ġ.ĝisjointed & duplicate data silos – 90-95% of the data in organisations is unstructured which lands in a data lake as it processes both structured & unstructuredĭata whereas Data warehouse processes only structured data – creating duplicate, out-of-sync data.Ģ. Each of them has its own challenges.Īnd keeping the data in two different platforms – Data Warehouse and Data Lake has its own challenges like duplication, synchronization of data, collaboration, security & governance, etc.īoth systems - Data Warehouse and Data Lake have advantages, but running parallel systems while going from reactive to predictive analysis introduces complexity that slows data operations. As you might be aware, data is generally stored in Data Warehouse and Data Lake. Now that we understand the data maturity flow, the question comes back to where to store it. This process is depicted in the diagram below, illustrating the sequential data flow. Subsequently, they proceed to ingest this data from the data lake into the data warehouse, specifically designed for BI use cases. To combine BI and AI use cases together, companies strive to first ingest their data into a data lake, which caters to AI use cases. As companies progress in making automated decisions, they gain a competitive edge, leading to exponential business growth. However, stage 5-7 relies on AI use-cases from data lake which helps companies understand and predict the future based on business constraints and how they can react in real-time. These phases rely on BI use cases from the data warehouse which holds historical data to produce valuable observations. If you see, step 1-4 is looking at the flashback to analyze what happened in the past. The data curve journey starts with cleaning the data from different data sources and then eventually leading to data exploration, and predictive analysis which will help in automated decision making which is the last stage. Are you aware of your position on the Data Maturity Curve? The journey towards data and AI maturity consists of various phases. The greater the level of maturity, the more successful they tend to be, thereby gaining an edge over their competitors. Moving from a reactive to a predictive approach, the level of maturity in data and AI greatly impact the competitive advantage of large enterprises.
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