Guide to Code-Free Data Ingestion for Your Cloud Modernization Journey

Jun 28, 2021 |
Mamun Reza

Data Engineering, Product Marketing

Co-authored by Preetam Kumar

Code-Free Data Ingestion for Your Cloud Modernization Journey

With the exponential growth of enterprise data, businesses today are facing a mammoth digital challenge. The traditional and legacy on-premises databases, data warehouses, and data lakes have failed to address the volume, variety, and velocity of data generated in the cloud. A recent study suggests that almost 73% of enterprises have failed to provide any business value from their digital transformation initiatives.

Migrating to Cloud

Many businesses move to cloud-based data warehouses and data lakes to modernize their data and analytics to address these challenges. However, one of the biggest roadblocks is ingesting data and hydrating their cloud data lake and data warehouses from various sources.

Organizations typically have large volumes of data in various siloed sources like files, databases, change data capture (CDC) sources, streaming, and applications. They need to quickly and efficiently move this data into a cloud data lake, cloud data warehouse, or messaging system before making it available for BI, advanced analytics, and AI or machine learning projects. They need to efficiently and accurately ingest large amounts of data from various sources in a unified approach using intelligent, automated tools to avoid manual approaches like hand-coding.

7 Common Data Ingestion Challenges

While data ingestion attempts to resolve the challenge of hydrating your data warehouse and lake from varied data sources, it is not without its own set of challenges. Here are seven data ingestion challenges:  

  • Out-of-the-box connectivity to sources and targets: The diversity in the data makes it difficult to capture data from various on-premises and cloud sources. Many analytics and AI projects fail because data capture is neglected. Building individual connectors for so many data sources isn’t feasible, since doing so requires significant time and effort to write code. Organizations need pre-built, out-of-the-box connectivity to easily connect to data sources like databases, files, streaming, and applications – including initial and CDC load.
  • Real-time monitoring and lifecycle management: It is extremely challenging to manually monitor data ingestion jobs to detect anomalies in the data and take necessary actions. Organizations need to infuse intelligence and automation in their data ingestion process to automatically detect ingestion job failure and set up rules for remedial action.
  • Manual Approaches and Hand-coding – The global data ecosystem is growing more diverse, and data volume has exploded. Under such circumstances, writing code to ingest data and manually creating mappings for extracting, cleaning, and loading data can be cumbersome and inefficient.
  • Schema Drift – Over time, sources and targets may change fields, columns, and more, resulting in schema drift. It is an inevitable by-product of the decentralized and decoupled nature of the modern data infrastructure. It follows that most data-producing applications operate independently, going through their lifecycle of changes. As these systems change, so do the data feeds and streams they produce. This constant flux creates havoc when trying to develop reliable continuous ingestion operations.
  • Speed – Because there is an explosion of new and rich data sources like smartphones, smart meters, sensors, and other connected devices, organizations sometimes find it challenging to get value from that diverse data. The challenge is even more difficult to overcome when organizations implement a real-time data ingestion process that requires data to be updated and ingested rapidly.
  • Cost – The infrastructure needed to support different data sources and proprietary tools can be very expensive to maintain over time. Maintaining in-house experts to support ingestion pipelines is costly. More significantly, businesses face disruption when they can’t make decisions quickly.
  • Compliance – Legal data compliance from countries around the globe has made it difficult for companies to sort their data according to regulatory compliances. For instance, European companies need to comply with the General Data Protection Regulation (GDPR), U.S. healthcare data is affected by the Health Insurance Portability and Accountability Act (HIPAA), and companies using third-party IT services need auditing procedures like Service Organization Control 2 (SOC 2).

Selecting the Right Data Ingestion Tool for Your Business

Now that we know the various types of data ingestion challenges, let’s learn how to evaluate the best tools.

Data Ingestion is a key core capability for any modern data architecture. A proper data ingestion infrastructure should allow you to ingest any data at any speed using scalable streaming, file, database, and application ingestion with comprehensive and high-performance connectivity for batch or real-time data. Below are the five must-have attributes for any data ingestion tool that you need to future proof your organization:

  • High performance – When you need to make big decisions, it’s important to have the data available when you need it. With an efficient data ingestion pipeline, you can cleanse your data or add timestamps during ingestion with no downtime. And you can ingest data in real-time using Kappa architecture or batch using a Lambda architecture.
  • Improved productivity – Efficiently Ingest data with a wizard-based tool that requires no hand coding onto cloud data warehouses with Cloud Data Capture capability to ensure you have the most current, consistent data for analytics.
  • Real-time data ingestion – Accelerate ingestion of real-time log, CDC, and clickstream data onto Kafka, Microsoft Azure Event Hub, Amazon Kinesis, and Google Cloud Pub/Sub for real-time analytics.
  • Automatic schema drift support – Detect and automatically apply schema changes from the source database onto the target cloud data warehouse to meet cloud analytics requirements.
  • Cost efficient – Well-designed data ingestion should save your company money by automating processes that are costly and time-consuming. In addition, data ingestion can be significantly cheaper if your company isn’t paying for the infrastructure or high skilled technical resources to support it.

How Can Informatica Help?

With Informatica’s Comprehensive, cloud-native  Mass Ingestion solution– you can get access to a variety of data sources by leveraging our more than 10,000 metadata-aware connectors. You can easily access the data to find it and ingest it to where you need it by leveraging Cloud Mass Ingestion DatabasesCloud Mass Ingestion Files, and Cloud Mass Ingestion Streaming. Combining that with database change data capture, application change data capture services, and so much more, you can trust you are getting the most up-to-date data for your business priorities. We offer the industry’s first and only unified platform for automated mass ingestion for files, databases, applications, and streaming with intelligent schema drift, automated structure derivation from unstructured data, and an easy 4-step ingestion wizard across multi-cloud, multi-hybrid environments. Our unified, wizard-based approach for ingesting data into cloud repositories and messaging hubs speeds database synchronization and real-time processing. And data ingestion is just one of the many data management features Informatica has to offer.

Informatica's unified, wizard-based approach for ingesting data into cloud repositories and messaging hubs.

Let’s look at a few examples of how Informatica collaborated with leading organizations to help them navigate the complexities of the multi-cloud world:

  • University of New Orleans (UNO) increases student enrollment and improves retention – Using Informatica Cloud Mass Ingestion, UNO accelerated its cloud modernization journey by quickly and efficiently migrating thousands of tables with complex data structures from Oracle to Snowflake without any hand-coding. The easy-to-use wizard-based approach helped UNO significantly reduce their manual ETL efforts by 90% and helped their developers build predictive models for advanced analytics to improve student recruitment, admission, and retention. UNO plans to ingest change data capture into Snowflake so that the latest data from Workday will always be available in the warehouse.
  • SparkCognition captures streaming data to improve machine learning models – Informatica enabled the customer to pursue new AI use cases, such as fraud detection. As datasets grow larger, customers will efficiently bring many data sources into their data science platform called Darwin using Informatica Cloud Mass Ingestion for predictive analytics and AI/ML usage.
  • An American multinational conglomerate corporation operating in the fields of industry, worker safety, U.S. health care, and consumer goods, chose Informatica’s Cloud Mass Ingestion capabilities, as it was the fastest and most scalable option to migrate data from existing legacy systems (SAP HANA and Teradata) and integrate huge amounts of data from across their enterprise platform into Snowflake.

Bottom Line

Data ingestion is essential for intelligent data management, and it allows organizations to maintain a federated data warehouse and lake by ingesting data in real-time and as a result make data-driven decisions. Watch the following demo videos to learn how to ingest databases, files, and streaming data, register today to try the free 30 – day trial for the Cloud Mass Ingestion service and fast-track your ELT and ETL use cases with free Cloud Data Integration on AWS and Azure.