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The Right ELT tool can make your Cloud Transition Faster, Easier and Efficient

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Big data is the buzzword that’s driving businesses today to adopt data platforms that can tap into this goldmine of unstructured and structured data effectively and efficiently. Moving your data to the cloud is proving to be the best solution owing to the plethora of benefits it offers. Among the many, you get agility in cost and infrastructure, which means paying for just what you use/compute. It is also extremely easy to scale up and down as you can get on-demand processing and infrastructure on cloud. One can start small and grow as per need.

The benefits of being on cloud certainly outweighs the transitioning efforts and time required to migrate to cloud. However, in order to reap these benefits, one must know how to structure their data architecture in the cloud as it’s much different than on-premise data. The other important factor to keep in mind is that moving to a cloud-based data lake or multi-cloud cannot happen overnight or in a jiffy, it’s a journey that one should embark on in a concise and systematic manner.

Problems that Arise during Cloud Migration

The data may come from varied sources and in various formats. Additionally, the cloud infrastructure is designed for generic use cases, hence it provides base components to build your use case and not necessarily the purpose-driven solution architecture. Therefore, sourcing and ingesting data from on-premise to cloud is quite a cumbersome process. The primary challenges faced by organizations migrating to cloud are:

Hence, selecting a migration platform that efficiently and effectively helps overcome these challenges is a key consideration. Now, let’s deep dive into CloudBlaze and learn what makes it a preferred platform over others.

CloudBlaze: The Right Blend of Tools for Easier and Efficient Data Migration

Among the many ELT tools available in the market is Microsoft’s CloudBlaze. CloudBlaze is an automated configuration driven data ingestion solution which acts as a data processor over Azure Data Factory and seamlessly does data ingestion. It ensures the following key business benefits.

1. Simplified Ingestion

It readily ingests structured, unstructured, internal, and external data from disparate sources, processes it for basic anomalies and neatly lays it out in one secure place. This reduces the time taken to ingest data and leads to quicker insights with minimal processing time.

2. Template-based Data Ingestion

Data ingestion is very quick as it’s template-based with no coding. This ensures reduced time to market and insight without the need for involving large engineering teams.

3. User-friendly GUI

It not only brings together disparate data sources, but enables the creation of data extraction patterns to create a use-case specific data mart. This ensures transparency and consistency of the data management process through a holistic web application that does all functions (such as data processing, auditing, balance, control, monitoring and governance on the data) in one.

4. Custom Pipelines

It integrates with HdInsight and Databricks to automatically create custom ADF pipelines without the need to understand complexities involved in building such pipelines. It helps in facilitating running of workloads on development, test and production environments. There is a fourfold enhancement in efficiency of data engineers through automatic creation of pipelines and activities. Moreover, there is 4x performance improvement and over 60% effort savings.

5. Supports a wide range of connectors

The platform provides variety of connectors which can cover most of the industry need. This ensure seamless integration and quicker deployment of solution.

6. Support for Power BI

It creates metadata on top of the data that’s getting provisioned into the Azure Data Lake Storage (ADLS). The data can be accessed via Spark (an execution engine) and power BI can effectively run on the ingested data. Helps in getting faster and better insights from business data.

7. Simple and Scalable Framework:

The framework simplifies the process of building an Azure data lake which can accommodate large amounts of data sets. The lake can be processed via a scalable execution engine using all types of integrations. Businesses can benefit from having all their workloads, analytics and applications working on a common data set or a multi-tenant platform.

8. Active Copy:

It goes beyond standard Extract Load Transform (ELT) by creating data as it appears on any given date in the source system. Hence, a 360-degree view of the data is available at all times for tracking inserts, updates and deletes. It saves businesses the time and effort of writing queries for viewing the current state of business. Active copy will be created from historical and current data sets. A copy of source data is available at all times for cross-referencing and carrying out predictive analysis.

Also, one needs minimum expertise on Azure Cloud to migrate their data from on-premise. CloudBlaze is available on Azure Marketplace for immediate proof of concept. To know more, please visit: https://appsource.microsoft.com/en-us/product/web-apps/rawcubes.rc-cloudblaze?tab=Overview

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