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The most important characteristic of an OLTP system is its ACID guarantees. This is normally the single source of truth for the system, where data entities are created, altered, and mutated. The first part of a modern data platform is there to support online transaction processing (OLTP). Before we dive into details, let’s examine the differences between the two and will explain exactly why you need both. OLTP vs OLAPĪ modern data platform is designed to accommodate OLAP systems, whilst those systems in turn partially rely upon OLTP systems.

Let’s look at how one can build a platform that helps deriving important insights from precious data, while keeping the cost-of-insight low so there is good return on investment to the business. Today these problems for most organizations are storing and querying large volumes of data at rest, analyzing and deriving insights from data in real-time as it streams through the data pipelines, and all of that while maintaining economic efficiency.

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In this series of blog posts, we’re going to examine the challenges faced by organizations and the methodologies and architectures available to them to address big data problems. Perhaps the biggest challenge of all is the most obvious: how do you deal with it? However, the challenges caused by big data are well known and well documented. Some technologies are more common and have market dominance over others, but frequently organizations will use varying technologies and methodologies without clear direction and strategy.Īs the tongue-in-cheek tweet above cleverly points out, the definition of big data is vague. The other challenge is that organizations are embracing new technologies, but there is a pervasive lack of knowledge or experience. One challenge is that the existing data platform uses legacy architectures or technologies, and is unable to scale, patch, and accommodate new requirements effectively.

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Whilst many software solutions exist to address the challenges posed by such quantities of data, this in itself poses one of two challenges. Data velocity is also an increasing problem for organizations, as they may not have the platforms or infrastructure in place to handle the data at the speed it’s generated or streamed.

The data itself presents untold opportunities and insights, but organizations consuming this data struggle to ingest, process, and query it in its vast volume.

The collectors are observability tools and data platforms, and the generators are users of applications and websites, IoT devices and sensors, and diverse levels of infrastructure. Day in, day out, more data collectors and more data generators are being built. Data is in every organization’s strategy, every engineer’s job description, and every CIO’s dreams (or nightmares). In this post, first in a series, we will look at how a typical Data Warehouse and Data Lake architectures are designed and built, and the technologies involved.

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