Jul 16th, 2024
by Alessandro Pireno
Picture this: It’s Monday morning, and your CEO urgently needs an update on the company’s website traffic.
You know the data exists, but it’s stuck in the slow-moving stream of your standard Extract-Transform-Load (ETL) pipeline. In a moment of desperation, you dump the raw data into a separate database for a quick analysis. The numbers look great! You share them with leadership, only to have the head of marketing question the results a few hours later when the data finally makes its way into the warehouse… and it doesn’t match.
Sound familiar? We’ve all been there. And like a band performing live, everyone knows when it just doesn’t sound right.
In the fast-paced world of real-time data, your team needs to behave like a band performing live – responsive to the audience, ready to improvise with the latest information at a moment’s notice. However, inconsistencies between data sources can lead to discordant notes, with different team members playing from different music sheets.
The struggle for real-time, unaggregated data is a common pain point in data science. Keeping separate data sources consistent is a headache, and discrepancies often lead to finger-pointing and confusion. But when the pressure is on to deliver answers now, you need a reliable way to access and analyse the freshest data.
Real-time capabilities are incredibly useful for:
Time-series data, in particular, often demands real-time analysis. Consider these examples:
Let’s explore three approaches to real-time data integration, each with its own strengths and weaknesses:
The Breakdown:
Pros:
Cons:
What is a Multi-Model Database (MMDB)?
A multi-model database is not your average data repository. It’s designed to handle diverse data types – structured, unstructured, semi-structured, time-series, geospatial, you name it – under one roof. Unlike traditional databases that specialise in a single data model (e.g., relational, graph, vector, time-series, or document), MMDBs offer a unified platform for storing, querying, and analysing different kinds of data.
MMDBs for Real-Time Analytics: A Paradigm Shift
MMDBs can massively accelerate real-time analytics by tackling the challenges that plague traditional data pipelines:
The Breakdown:
Pros:
Cons:
Workload Isolation: The Key to Performance
One of the most critical features that has emerged since the adoption of cloud-native applications is the ability to isolate computational workflows from high-concurrency capable storage platforms. This capability ensures that the complex queries and analyses run by data scientists don’t impact the performance of transactional workloads essential for business operations. Workload isolation acts as a safeguard, allowing both worlds to coexist harmoniously within the same database.
The Breakdown:
Why Choose This Approach?
This approach is ideal when:
Pros:
Cons:
The ideal architecture depends on YOUR unique needs. Consider the following:
It’s important to recognise that different data tasks call for different tools. While OLAP warehousing is invaluable for analysing large historical datasets, it’s not always necessary (or efficient) when dealing with current, often smaller-scale data. Waiting for data to trickle through a lengthy processing pipeline simply isn’t an option when you need real-time insights.
Traditional architectures for real-time data can be likened to musical ensembles. The Real-Time Streaming Platform + ETL Tools approach, with its separate components for streaming, ETL, and storage, resembles a chamber ensemble. Each instrument plays a distinct role, offering flexibility but requiring careful coordination. The Multi-Model Transactional Database approach is akin to a jazz band: it’s agile and responsive, improvising on real-time data, but may lack the structure of a larger ensemble. In contrast, the Single Platform with Workload Isolation approach is like a symphony orchestra: diverse capabilities within a unified system work harmoniously under a skilled conductor, delivering power, precision, and a seamless experience. While each approach has its strengths, the symphony orchestra’s unified power may be the key to unlocking new levels of real-time data performance.
No matter your choice, remember, real-time data is the fuel for today’s data-driven decision-making. Choose the architecture that empowers your data science team to deliver maximum value to your organisation.