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Timeflow Partner With OpRes To Improve Operational Resilience In Financial Services

Timeflow are pleased to announce our strategic partnership with OpRes, a business that focuses on the emerging “Operational Resilience” agenda within financial services. The aim of Operational Resilience is for banks to properly identify where individual business service lines are exposed to risk due to their dependence on third party systems or suppliers. As an

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Step Away From The Dashboard

I often comment that dashboards are limiting the potential of what businesses achieve with data and analytics. The idea of a dashboard always sounds and looks appealing, and of course they capture managements attention, so we run headlong into building them without asking if there is a better way to achieve the business outcome. Once

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Emitting Apache Druid Metrics To Kafka

Apache Druid captures detailed metrics about how it is operating and performing, including query performance, memory usage, CPU utilisation, ingestion speed and more. We may need to capture and monitor these metrics in order to investigate problems or identify proactive optimisations which can improve the performance of the cluster. In this article we explain how

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What Is Kafka Streams?

Kafka Streams is the component of the Kafka ecosystem which can be used for stream processing. This involves taking messages from Kafka, usually as they are produced in real time, and processing or responding to them in some way. Common examples of this include: Calculating analytics over event streams e.g. tracking the average order value

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Streaming Technology – Sourcing Events

In order to move towards Event Streaming and Event Based Architecture, the first thing we need to do is source data in real time and publish it onto an event stream. Even this first step can be difficult, because many applications do not “publish” events as they are entered by users. Instead, these applications interact

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Free Guide – An Overview Of Streaming Data Technology

Many companies are looking to use their data more effectively in order to improve their customer experience and the efficiency of their business. Speed is an important part of this. The earlier you can respond to incoming data, the more opportunities you have to improve the customer experience. Recognising this, businesses are implementing Streaming Data

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Databricks Structured Streaming Example

Spark 2 introduced the concept of structured streaming, giving users the ability to process streams of unbounded data using higher level abstractions. This is an extremely powerful capability which allows data engineers to do streaming transformations and analytics over data as it is ingested, and potentially join and integrate this with batch data at rest.

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Why Real Time Analytics Is More Than Just Faster Business Intelligence

In many situations, the earlier we respond to incoming data the better.  This might be in a genuinely real time situation such as a self driving car, a trading system or a fraud check, or a more vanilla business scenario such as a product out of stock which we hope to inform our users about

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Moving From Batch To Streaming Extract, Transform and Load

There are many situations in Enterprise IT where we need to move, copy or integrate data.  For example, populating a centralised data warehouse or data lake, integrating two systems such as an ecommerce and CRM system, or exchanging data between partner organisations.    Moving data in this manner is referred to as Extract, Transform and

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Why Serverless Is The Future For Data & Analytics Platforms

In the bad old days, the first step when building a database, analytics or business intelligence solution would be to order or provision a number of servers for hosting.  Particularly in the data world, you would have to worry about capacity planning and over provisioning of infrastructure to account for future volumes of storage and

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