Cloud and AI are Driving a Change in Data Management Practices
Data is the new currency, and it’s giving rise to a new data-driven economy. Some recent studies have found that an S&P 500 company’s average lifespan is now less than 20 years – down from 60 years in the 1950s. Almost all industries are changing their business models to derive maximum value from data, which is critical for their growth and survival. Plus, the emergence of unstructured data such as IoT, machine logs, and social media data has huge potential for analytics. To thrive in a data-driven economy, enterprises should think about their approach to data ingestion – in particular, they should consider a cloud mass ingestion solution. Here’s why.
Data Ingestion and the Move to Cloud
An industry study reports 83% of enterprise workloads are moving to the cloud, and 93% of enterprises have a multi-cloud strategy to modernize their data and analytics and accelerate data science initiatives. These market shifts have made many organizations change their data management approach for modernizing analytics in the cloud to get business value from the high volume of unstructured and semi-structured data.
The democratization of data increased volume of data, growth of unstructured and semi-structured data, streaming data, and the need for a cloud data warehouse and data lake to drive next-gen analytics and AI/ML use cases the significant reasons for this shift in data management practice. The need to operationalize machine learning algorithms and data platforms also put a high demand on data management.
However, a cloud data warehouse or data lake requires timely and trusted data to deliver results that your management can depend upon for critical decisions and interactions. Ingestion solutions can help move data into your cloud data warehouse or data lake, but taking the right approach to ingestion matters. What is needed is a cloud data management solution that can manage the challenges of data volume, complexity, security, trust, and velocity so that you can enable your data engineers to build data pipelines rapidly, developers can focus on business logic, and data scientists can build machine learning models for delivering breakthrough insights.
How AI-Powered Intelligence and Automation in Data Management Improves Operational Efficiency and Drives Business Value
An intelligent and automated cloud-native data management solution is the answer to enable organizations to get maximum benefits of modernizing analytics in the cloud and unleash the full potential of cloud data warehouses and data lakes across a multi-cloud environment. It can help organizations improve operational efficiency, increase productivity, and lower the total cost of ownership with best-in-class data integration, data quality and governance, and metadata management.
A cloud data management solution consists of an end-to-end process to catalog, ingest, process, prepare, transform, and enrich structured, unstructured, and semi-structured data in a governed manner. It helps to get access to and consistently manage data across different data types in a company. The purpose of a cloud data management solution is to fulfill all data requirements for next-gen advanced analytics use cases, applications, and business processes in a company.
An intelligent and automated cloud data management solution helps build and manage workloads in the cloud. Organizations can improve data transparency, connect to diverse data sources, and manage increasingly complex multi-cloud environments. This approach enables people across your company—from business analysts to data scientists and data engineers—to access high-quality data quickly and easily for their analytics initiatives, driving innovation and providing organizations with a competitive edge.
Now, let’s deep dive into the data ingestion capability of Informatica’s cloud data management solution, along with a real-world customer use case.
Faster, Simpler, and Low-Cost Mass Ingestion with Informatica
Informatica Cloud Mass Ingestion provides a simple and intuitive wizard-driven experience for ingestion, increasing productivity, and lower costs over an approach based on hand coding.
Without having to code data pipelines, you can ingest any data—such as real-time streaming and IoT data or transaction change data (CDC) and bulk data – from on-premises systems such as file systems, mainframes, relational databases such as Oracle, SQL Server, and MySQL, and data warehouses like Teradata. Using scalable streaming and mass ingestion methods with comprehensive and high-performance connectivity for batch, streaming, or near real-time, Informatica Cloud Mass Ingestion can move the data onto cloud data lakes and cloud data-warehouses such as Snowflake, Azure SQL Data Warehouse, and Amazon S3, as well as messaging hubs such as Apache Kafka. All while percolating changes in the source schema automatically onto the target. And you can monitor the ingestion jobs in real-time along with lifecycle management and alerting capabilities.
Real-World Mass Ingestion at the University of New Orleans
Despite its reputation for excellence and recognition as a Research University by the Carnegie Foundation, the University of New Orleans (UNO) faces the same challenges as other schools when attracting and retaining students. To appeal to students’ interests, it needs to know more about them and what they are looking for in a degree or certificate. For instance, if a student drops out, historical data might reveal patterns or events that explain why, but only if that data is made available for analytics to build a business case for changes in student retention policies.
UNO created a centralized cloud data warehouse to enable more effective analytics, accelerate cloud migration, and address potential information security issues. It selected Snowflake as its cloud data warehouse. But to move quickly and reduce the possibility for human error, UNO needed a cloud data integration solution that could rapidly extract and transform data from various sources and load it into Snowflake.
The university used Informatica Cloud Connectors to connect its legacy systems to the cloud and bring Workday data into Snowflake without hand coding, saving time and IT resources. To jump-start its cloud migration, it used Informatica Cloud Mass Ingestion to quickly ingest 600 gigabytes of historical ERP data from Oracle into Snowflake. They were able to generate hundreds of mappings in a very short time and saved time and resources on their ETL effort.
To learn more, join our webinar with the University of New Orleans – How the University of New Orleans modernized its Cloud Data Warehouse with Informatica’s Cloud Mass Ingestion on December 8 at 8 am PST. We will also showcase a live database ingestion demo using Informatica Cloud Mass Ingestion.