Data Engineering
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Most businesses have undergone a digital transition which has resulted in numerous new data sources and considerably more complex data at a much greater frequency. While it has always been accepted that data scientists are required to make sense of all the data, data engineering is a process that is often overlooked and yet is a prerequisite to any effective data analysis. Data engineering is effectively the organisation of data sources, with the aim of maintaining quality, security, and availability of data. With the onslaught of corporate digital transformations, the Internet of Things, and the rush to become AI-driven, it is evident that data engineering plays a key role, with an ultimate purpose to offer structured, consistent data flow to allow data-driven activities, such as developing machine learning models, analyzing exploratory data and using external data to populate fields in an application
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Engineers design and build things. Data engineering pipelines that transform and transfer data into a relevant format are designed and built by data engineers. They then integrate, consolidate, and cleanse data so that it can be structured for use in analytics applications, ensuring that it is highly useable by the time it reaches Data Scientists or other end users.
Benefits and values generated
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Increasing data pipeline throughput
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Data warehousing for scalable analytics
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Building a real-time data platform
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Ensuring data is secure in motion and at rest
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Automating data compliance
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Auditing
Data Engineering services
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We provide an end-to-end approach for mapping all potentially important and frequently unstructured data sources, as well as identifying essential data platforms and owners, to allow data collection, preservation, analysis, and review. We also present a best practises roadmap to assist enterprises in properly managing massive amounts of data. The evaluation helps our clients in implementing an evolved data strategy and solution to improve their data analytics operations and consistently deliver business insights.
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A modern data architecture requires an updated technological stack. Traditional databases and data processing technologies are incapable of dealing with the massive amount, variety, and velocity of data generated in the digital age. We can assist in the establishment of a Big Data Architecture that makes use of emerging technologies such as serverless platforms, AI, and ML to efficiently handle large and complex data sets, as well as to create data architectures capable of realising new and improved business outcomes while driving significant cost out of the IT budget.
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Through virtualization, a scalable cloud architecture is made possible. Our clients choose us because our cloud solution architects can set up a cloud computing architecture that can scale up or down the resources as needed to satisfy shifting demand. Scalability is one of the cloud's distinguishing features and the key driver of its growing appeal among enterprises.
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One of the most difficult challenges for real-time processing systems is ingesting, processing, and storing data in real time, especially at high volumes. Processing must be done in such a way that the ingestion pipeline is not blocked. Another problem is the ability to act quickly on data, such as producing alerts or presenting data in a real-time dashboard. Batch Processing is one such way for efficiently handling large volumes of data and sending it to the destination system in batches. It is a versatile strategy that gives you more control and aids you in effectively transmitting data using the computing resources that are currently accessible.
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