May 12, 2021

By the end of next year, more than half[1] of enterprise data will be created and processed outside of data centres — and outside of the cloud. With so many data sources and hybrid environments to manage, big companies are finding it harder to control data quality.

Since legacy IT infrastructure often lacks the ability to effectively discover, understand, and govern the information held in disparate systems and formats, laying the groundwork for modern analytics is almost impossible.

To overcome that problem, organisations have invested in cloud data warehouses or created data lakes. But those models are also straining under the weight of exponential data growth and complexity.

While cloud data warehouses are optimised for structured data, they struggle to handle unstructured and semi-structured data.

Data lakes were designed to overcome those limitations and accommodate data in various formats. But lack of transaction support and an inability to enforce data quality means data lakes can't keep up with modern analytics requirements.

So a new model has emerged that combines the best aspects of both: the cloud data lakehouse.

The lakehouse combines the best elements of data lakes and data warehouses, blending data structures and data management features similar to what you’d get in a data warehouse, and applies them in the same low-cost storage used for data lakes. The result is a single repository for data of all kinds that can be read and augmented by machine learning and AI.

The lakehouse offers enterprise-scale data integration, data quality, and metadata management suited to today’s analytics. It also enables today’s governance requirements by managing discovery, cleansing, integration, protection, and reporting across cloud environments.

Getting from ambition to implementation

 

With Microsoft Azure, enterprises can bring data warehouses and data lakes' capabilities together for a unified platform that ingests, stores, processes, enriches, and serves data for business intelligence and machine learning.

Creating a data lakehouse and implementing applications like Microsoft Azure Synapse Analytics and Microsoft Power BI delivers on essential business requirements — the first being cost. Azure Synapse can deliver price-performance that's 94 per cent less[2] than other vendors.

It also brings together data integration, enterprise data warehousing, and big data analytics at cloud scale. Unifying those workloads can significantly reduce development time and speed up access to usable insights.

Looking to the future, Synapse has already integrated machine learning capabilities. Data engineers working in Synapse can use the templates in Microsoft Azure Machine Learning's central registry to build new models and start using predictive analytics faster.

Of course, before these benefits can be realised, the data has to be migrated from current repositories into the new cloud environment. Agile Solutions recommends a phased approach based on our proven methodology.

Successful implementation of a cloud data lakehouse can deliver the kind of insight-led, data-driven decision making modern digital businesses need to compete and survive — all the more reason to trust data migration and lakehouse cloud infrastructure to the right vendor.

How Informatica complements Azure

 

Informatica is the leading integration platform as a service (iPaaS) vendor. It has been rated number one by Gartner for seven years running, and its cloud data integration offering is one of the most modern and comprehensive available.

The Informatica iPaaS platform is cloud-native, microservices-based, API-driven and AI-powered. Informatica also uses an AI engine called ‘Claire’, which uses metadata and machine learning to accelerate time to value. Large enterprises, businesses with hybrid environments, and organisations who may have used Informatica’s PowerCenter solution in the past will find that migrating cloud data with Informatica will accelerate the move to Azure.

Globally, the Informatica cloud supports more than 15 trillion transactions every month and holds the industry’s highest number of security certifications. It offers a broader range of connectors than any other iPaaS vendor and works seamlessly with Microsoft Azure solutions.

While many companies choose Azure as their cloud platform, 93% of enterprises are on a multi-cloud footing. Since Informatica is platform-agnostic, as a tool to manage cloud data migration, it pairs neatly with a varied infrastructure approach. In fact, it's commonly requested by Agile clients for demos and proof-of-concept work.

A unified infostructure for digital business

 

To take advantage of data and turn it into actionable insights, today’s organisations need a unified repository that can manage data across environments and then act as a mechanism for delivering insights and information to users and applications.

A cloud data lakehouse delivers fast and contextualised access to the data businesses need. That way, they can generate analytics that point to changes in customer behaviour, investment opportunities, activities with a higher propensity for risk, and emerging market trends.

By pairing the tools offered by Microsoft Azure and the Informatica cloud, enterprises can achieve an immersive analytics experience from pipeline to visualisation.

Want to learn more? Download our latest white paper: Why the time is right to implement a cloud data lakehouse.

[1] Gartner: The Future of IT Infrastructure Is Always On, Always Available, Everywhere

[2] Microsoft and GigaOm Analytics Field Test 2019