As companies are moving more data to the cloud than ever before, data ingestion services have evolved to keep up. Cloud-ready AI-driven data management & data analytics help businesses grow and gain a competitive edge. According to Flexera’s State of the Cloud Report, 54% of organizations use cloud data warehouses to help drive their digital business.
Today’s IT teams must fully manage the “4 V’s” of big data: data volume, data velocity, data variety and data veracity. Data variety may include traditional databases, mainframes, on-premises data warehouses and streaming data. That’s why moving data into a cloud data warehouse or data lake can be challenging. Data ingestion solutions solve this problem. IT teams can ingest all the data they need; enabling data pipelines to manage data flow at scale.
This blog post is part of a series on cloud mass ingestion. Check out these other posts on data ingestion tools:
- Critical Use Cases of Cloud Application Ingestion
- Speed Up Data-Driven Decision-Making with Application Cloud Mass Ingestion
On-Premises Data Warehouses Can't Keep Up
On-premises data warehouses are too complex and costly to handle modern cloud data analytics and AI. That’s because on-premises data store methods are:
- Resource-demanding – Need dedicated hardware installed and managed by the IT team
- Expensive – Costly to set up, operate and scale
- Labor-Intensive – Challenging to use, sometimes taking months to upgrade
- Complex – Only experienced database administrators can maintain them
- Inflexible – Rigid or limited data models
Benefits of Moving to a Cloud Data Warehouse
Data pipelines in the cloud help IT manage the “4 V’s”. New cloud data architecture advancements make the data ingestion process easier. Here are some pros of moving to a cloud data warehouse:
In the cloud, companies can track customer behavior in real time. It lets them combine new application data with historical buying patterns. Then they can deliver the right product to the right customer at the right time. The result? A better customer experience.
For example, one focus of the telecom industry is to reduce customer churn. When big data is in the cloud, telcos can capture customer location data in real time. They can predict service disruptions. They can also notify SLA customers of outages via text or email with a timeline for resolution.
In the cloud, companies can access real-time streaming data. This helps them avoid product issues. Sensor data generated from Internet of Things (IoT) devices lets them fix problems. As a result, the customer is never even aware there was an issue.
For example, cloud data helps in manufacturing. If a turbine’s temperature exceeds 100 degrees centigrade, rules can be put in place in the data flow to avoid issues. Thanks to real-time alerts from the system, the operations team can then take swift action.
Analyzing events in a cloud data warehouse is a business advantage. You can reduce costs, boost margins, streamline processes and respond to market needs.
The food industry offers a good example of improved operational efficiency. With predictive analytics, they can calculate average checkout wait times. This helps them better understand customer behavior.
In the cloud, innovations in machine learning (ML) and AI are helping businesses deliver better customer service. The entertainment, auto and retail industries are all applying cloud data innovation. Ride-sharing apps, OTT and ecommerce platforms are good examples.
Ingesting Application Data Is Critical for the Success of Your Cloud Data Warehouse
In the on-premises world, organizations may operate only a handful of applications containing data that is valuable to them. In the cloud, companies may have hundreds or thousands of data-rich applications. And every application or group may have their own data silos. Product data, marketing data and finance data may each be located in different systems. As a result, none of the data can be fully integrated for analytics consumption.
Let’s walk through some common use cases for ingesting application data into a cloud data warehouse:
Real-time offers – Retailers can combine customer transactions and spend history. They can provide targeted real-time customer offers and alerts. This increases revenue via cross-sell and up-sell.
Customer fraud analytics – Banks can alert their customers of potential fraud with data based on ML.
Predictive maintenance – Manufacturers can identify stress signals from devices. They can act on them early to optimize costs and maximize availability.
Clinical research optimization – Healthcare companies can collect and process bedside monitor data. This helps clinical researchers detect and understand disease markers.
How Can Informatica Cloud Mass Ingestion Help with Data Ingestion?
Informatica Cloud Mass Ingestion is a code-free, cloud-native data ingestion service. It is available through the Informatica Intelligent Data Management Cloud (IDMC). Cloud Mass Ingestion provides format-agnostic data movement. It enables mass data ingestion and cloud mass application ingestion. Features include file transfer and exactly-once database replication. Cloud Mass Ingestion offers mass streaming ingestion from various sources. This is complete with real-time monitoring, alerting and lifecycle management.
Benefits of Informatica's Cloud Mass Ingestion:
Speed: Build data ingestion jobs in minutes with an easy-to-use four-step wizard.
Simplicity: Simplify data ingestion with out-of-the-box connectivity.
Scale: Ingest terabytes of data in real time and at scale.
Flexibility: Track, capture and update data with automatic drift detection and synchronization.
To move data to the cloud, a data ingestion service is needed. Data ingestion is the process that lets you ingest data in the cloud from many data formats.
Get started with the Informatica Cloud Mass Ingestion service. Experience actionable data analytics that maximize the value of your AI initiatives.