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