Simplicity, Productivity, and Scale for Cloud Data Warehouses and Data Lakes
How to Achieve Cloud-Native Analytics Nirvana
Cloud is a key enabler to every digital transformation initiative, and we’re seeing the adoption of cloud increasing exponentially. Over 80% of workloads are expected to be run in the cloud this year and the same number are expected to move between clouds (Source: Forbes, Logic Monitor Cloud Adoption).
But why do most cloud data warehouse and data lake projects fail to deliver expected results?
As enterprises build, consolidate, or modernize analytics in the cloud, a key concern is the data. The problem is many organizations struggle to see the expected time to value and ROI from their cloud data warehouse and data lake due to lack of holistic and end to end data management.
Let’s take a close look at some of the challenges with current data management approaches:
One of the key challenges that organizations face while modernizing their data warehouse or data lake in the cloud is how to easily and rapidly ingest and hydrate these from various siloed sources like files, databases, streaming, and IoT devices. The next challenge comes once they land their data in a cloud-native data warehouse or data lake. How do you quickly and seamlessly access, manage, share, and move different workloads at scale from one cloud to another? How do you transform it for analytics consumption, especially when your data is in different formats, has varying dimensions, and changes often? How do you make trusted data available for everyone? How do you make it secured and governed?
As customers drive their cloud-native data warehouse and data lake implementations, 78% seek data and analytics solutions that are easy, reliable, and simple to use. Many engage in manual hand-coding, which renders them unproductive, slow, and exposed to risk. Administrative overhead of cloud data management infrastructure further slows them down and adds to costs. There is an acute need to scale up to handle the petabytes of data that will hit them hard in the near future.
With cloud-first, cloud-native data management you can accelerate your cloud analytics
Without taking a systematic and integrated approach to data quality and data management in their cloud data warehouses and data lakes enterprises will not be able to accelerate time to value, reduce costs, improve efficiency, increase scale, add flexibility, and deliver trusted, real-time insights for business decision making. A focused and comprehensive cloud-native data management strategy helps you realize faster time to value and ROI—whether you’re modernizing, consolidating, or just getting started in the cloud. It also future proofs your business with a multi-cloud strategy spanning the cloud platforms your business runs on: Amazon Web Services, Google Cloud, Microsoft Azure, Snowflake, Databricks, and more.
Gain Simplicity, Productivity & Scale with AI-Powered Cloud-Native Data Management
How do you accelerate your cloud analytics initiatives, to deliver the right data to the right person at the right time in the right format?
Only intelligent, automated, cloud-native data management can deliver the simplicity, productivity, and scale you need to succeed.
Informatica offers the industry’s leading intelligent and automated, cloud-native data management with expanded ingestion and processing with advanced serverless and elastic capabilities on Spark for any type of workloads that aims to drive simplicity, productivity, and scale for our customers’ cloud analytics journey.
At our Winter launch event, we have added some exciting new enhancements to Informatica’s Cloud Data Warehouse and Data Lake Solution that aim to simplify data management with advanced serverless capabilities, democratize and automate productivity for all users, and achieve unprecedented elastic scale for your data management pipelines – so you can accelerate your path to cloud-native analytics.
Informatica’s advanced serverless data management deployment eliminates the need to manage hardware or software; it simplifies DevOps, DataOps and MLOps, enables developers to focus on the business logic, provides agility to quickly deploy new data pipelines and help speed time to value, automatically scales the clusters for cost optimization, and delivers high performance with intelligent auto tuning.
Some of the key highlights from Winter launch include:
Serverless, zero-code simplicity: Informatica is the only cloud-native data management vendor with no hardware or software to manage. Wizard driven, consistent experience for bulk and real-time ingestion from variety of sources and easy drag-and-drop design experience with 100+ prebuilt functions and templates across the entire data development pipeline.
Intelligent, automated productivity: Save 70% of your data engineers’ time every month with a dynamic mapping framework for greater reuse of mapping logic, automatic schema drift support, auto change data capture (CDC) to ingest data, Spark job auto tuning and more.
Unprecedented elastic scale: Informatica delivers AI/ML driven auto-scaling for your data pipeline, advanced pushdown optimization driving upto 50x performance improvement, automated bulk load and incremental change data capture for ingesting data from tens of thousands of database tables, and Spark-based elastic processing engine to process 3TB data under two hours.
With Informatica, you can not only ensure success for your cloud data warehouse or data lake projects, but also rapidly accelerate time to value and ROI for your cloud analytics initiatives.
And check out our solution brief, Modernize Your Data Warehouse and Data Lake in the Cloud.