By employing data science techniques, you can analyse a large number of data samples and find patterns that give relevant information about problems that are too complex for humans to discover and extract manually.
The fact that businesses from a wide range of industries can benefit tremendously from the appropriate use of already existing internal data through AI algorithms is becoming a globally accepted truth. This three-part blog series will explore Azure Databricks and AI solutions and their benefits for businesses across industries.
What is Azure Databricks?
Azure Databricks is a Big Data technology that Microsoft integrated as one of its services in Azure Cloud. In a nutshell, Databricks is a Big Data tool that helps the data analytics team members code quickly, in a scalable cluster, which is straightforward to configure, rather than relying on complex knowledge of distributed computing.
Databricks was developed as a technology starting from the Apache Hadoop project. The founder of Databricks technology is the Romanian engineer Matey Zaharia that started the Spark project in 2012.
At its core, Databricks has the Apache Spark technology, having the data spread across distributed systems without compromising the data integrity. Spark is mainly performing map-reduce operations but is performing the computations in memory. Spark is essentially providing an Advanced Programming Interface (API) on top of a distributed computing layer, allowing data manipulations, grouping data, normalising data and applying common data transformations. In data science projects, the first stage of the work focuses exactly on this type of transformation and is typically known as the Extract Transform Load (ETL) stage.
Common challenges faced by AI solutions
Some of the frequent challenges that the AI solutions face concerning on-premises ETL pipelines are:
Production-ready ETL pipelines can become slow due to the availability of on-premises computer cluster capacity and the cluster properties, such as CPU, network and disk performance.
4 Reasons on-premise ETL pipelines are complex to maintain
Scalability: as data volume and velocity in a company are increasing with time, the ETL pipelines need to scale upward to meet the processing, storage and memory requirements.
Data Integrations: multiple sources of information inside a business, such as the financial and management department must be combined seamlessly, ideally automatically, without data engineer involvement.
Cost: human specialists for maintenance of on-premises infrastructure involves a continuous expenditure, while the need for AI predictions is often not continuous.
Reliability: there are multiple reasons why on-premises big data ETL pipelines can fail. The most common issues are failure of a cluster node, changes to data source connections, loss of a disk in a storage array, power interruption, increased network latency, temporary loss of connectivity, authentication issues and changes to ETL code or logic.
At Amdaris, these issues are addressed with the analysis and interpretation of business objectives using a holistic approach so nothing is missed, starting with the details and moving towards production-ready solutions.
If you would like to speak to someone at Amdaris about AI solutions, just get in touch.
Call +44(0)117 935 3444 or contact us using the form below and let us know about your next plans. We will help you choose the best technology for making your project a success.
Keep an eye out for the next part of this blog series all about the Amdaris AI accelerator.