5 Things to Consider for a Cloud-Native Data Management Solution

Last Published: Aug 05, 2021 |
Preetam Kumar
Preetam Kumar

Product Marketing Manager

What is cloud-native data management?

Today, organizations are either building new cloud-native data warehouses or data lakes or modernizing their existing on-prem systems in the cloud to accelerate their digital transformation journey. According to Gartner, by 2025, over 80% of organizations will use more than one cloud service provider (CSP) for their data and analytics use cases.

But, just moving the data to the cloud doesn’t solve their business or technical challenges. Data management is still a huge blocker for enterprises to get maximum ROI from their cloud-native data warehouse and data lake investments.

The data management challenges in the on-premises world still exist in the cloud. Organizations need to move away from their traditional hand-coding and siloed data management approach. Instead, they should embrace an intelligent AI-powered cloud-native data management solution that can ingest, catalog, integrate, apply data quality rules, and prepare the data in a governed manner to make it available for next-gen use cases in a democratized way.

On-premises data management vs cloud-native data management

On-premises data management works well for on-premises workloads and legacy systems such as Teradata, Mainframes, Netezza, Oracle, etc. It is used for on-prem data integration, including data warehousing, data analytics, data governance, and data quality, as well as populating and updating the on-prem data warehouse. On-prem data management effectively collects massive amounts of data and integrates the data in an on-prem Hadoop data lake, so teams can derive insights for better decision-making.

However, on-prem data warehouses and data lakes are not designed to support modern analytics use cases like data science and AI/ML. They are not equipped to support high-volume data, different data types – structured, unstructured, mobile, social, IoT – or quick data access to new users like data scientists, data analysts and line-of-business teams.

Forward-looking organizations want a modern cloud-native architecture that can enable a long-term strategy for maximizing their data assets based on a multi-cloud platform. To achieve this goal, they’re modernizing their on-prem data warehouse and data lakes in the cloud, such as Amazon Redshift, Azure Synapse, Google BiqQuery, Snowflake, AWS S3, Azure Data Lake Storage, and Google Cloud Storage.

To get maximum ROI from their cloud data warehouse and data lake investments, they need AI-powered, cloud-native data management to build a foundation of clean, trusted data that allows them to uncover hidden insights.

Benefits of cloud-native data management

The cloud holds the promise of increased agility as well as lower total cost of ownership and risk – not to mention the ability to scale fast – which is why enterprises are investing heavily in new cloud data warehouses and cloud data lakes (e.g., AWS S3, ADLS, GCS, Databricks Delta).

So, how do you accelerate time to value and increase ROI for these investments you have made in the cloud? A data management platform built on a cloud-native, AI-powered, microservices, and API-based platform helps you accelerate time to value, increase ROI, and succeed in your consolidation or modernization initiatives. Here are the benefits of implementing a cloud-native data management solution:

  • Demonstrate rapid ROI with faster first time to value by ensuring timely completion of the data warehouse and/or lakes migration to the cloud
  • Increase productivity and save costs with an integrated and comprehensive data management solution that delivers intelligence and automation
  • Minimize risks of the consolidation or modernization initiative by avoiding the challenges that come with using hand-coding and multiple point solutions to address data management issues
  • Gain cloud scale and agility with the rapid deployment of jobs, faster DevOps, DataOps and MLOps, automatic upgrades, fast data onboarding, and an integrated solution for high availability and advanced security
  • Improve visibility of data by connecting and scanning metadata for all types of databases, SaaS apps, on-premises apps, on-premises data warehouses, ETL tools, BI tools, and more to provide complete and detailed data lineage
  • Successfully deploy new data warehouses and data lakes in the cloud with high-performance data integration that connects to all data and seamlessly integrates high volumes of data for any analytics workload

What should you consider while evaluating a cloud-native data management solution?

Above all, an enterprise cloud-native data management solution should incorporate intelligence and automation to make data integration and data management simple. And it should offer a broad set of capabilities for a multi-cloud environment, including data integration, data transformation, data ingestion, application and API integration, data quality, data governance, metadata management, master data management, and data security.

Let’s look at the critical capabilities you should consider in a cloud-native data management solution:

Analytics modernization:

Every company needs to make better and faster business decisions by quickly analyzing data in various formats (e.g., structured, unstructured, mobile, social, and IoT data). Organizations are moving to cloud-native data warehouses and data lakes to modernize their data and analytics approach. Look for a cloud-native data management solution that can meet the challenges of data volume, velocity, and variety and provide trusted data for accelerating your digital transformation initiatives.

Application modernization:

Similar to data warehouse modernization, companies are also modernizing their legacy applications (e.g., Seibel CRM, PeopleSoft HRMS) in the cloud and investing in cloud-native applications (e.g., Salesforce, Workday, and Adobe) to drive business agility and create competitive advantage. Make sure your cloud-native data management solution can connect applications and services from cloud and on-premises environments via self-serve capabilities.

Enterprise-scale iPaaS:

Many enterprises struggle to achieve their digital transformation goals because their data and applications are disconnected. Organizations need both application integration and data integration to automate processes and share real-time and batch data. Double-check that the cloud-native data management solution you’re evaluating has a comprehensive integration platform as a service (iPaaS) that supports multicloud integration, API, data and app integration with zero code, data management patterns such as B2B, integration hubs, data quality, mass ingestion, master data management, data catalog, and data streaming.

Master data management (MDM):

Business insights are necessary to fuel an organization’s digital transformation initiatives, such as improving customer experience, optimizing digital commerce, streamlining the supply chain, and implementing advanced analytics. Seek out a cloud-native data management solution with MDM as a service to drive business value from master data by creating trusted data for the organization. Be sure it ties all systems and information together into a single source of truth.

Data governance:

Before implementing a cloud data warehouse or data lake, organizations need to govern the data and prevent the data lake from becoming a data swamp. Look for a cloud-native data management solution with a robust data governance capability to ensure high-quality, trusted data is available for all business users to run analytics and unlock valuable business insights.

Get started with cloud-native data management

As organizations embark and continue their data-driven digital transformation journey, they’re centralizing their data and analytics in cloud data warehouses and data lakes to drive next-generation analytics and data science use cases. To do that, they need an enterprise-scale, cloud-native data management solution to help them rapidly develop and operationalize end-to-end data pipelines and modernize legacy applications for AI.

To find out more, please visit our Intelligent Data Management Cloud page.

First Published: Apr 05, 2021