Why is data ingestion critical for cloud modernization?
How is data ingestion different from data integration?
Data ingestion use case patterns
Watch out for these common data ingestion and synchronization challenges
Data ingestion for ETL and ELT
Choose the right solution: 7 essential data ingestion capabilities
Why Informatica Cloud Mass Ingestion?
Data ingestion customer stories
Get started with unified data ingestion
To accelerate their data and analytics practice and drive competitive advantage, many organizations are focusing on cloud modernization. They have already started their modernization journey by migrating their existing data warehouses and data lakes to the cloud. But cloud modernization doesn't mean just migrating on-prem data to the cloud – it is about taking advantage of cloud infrastructure and services to accelerate digital transformation for delivering business value.
Modernizing data and analytics in the cloud helps organizations accelerate their artificial intelligence and advanced analytics initiatives to drive critical business decisions and innovation. As organizations embark on their cloud modernization journey, they face challenges around legacy data and an increase in disparate data sources and data volume, velocity, and integration silos. However, one of the biggest roadblocks is data ingestion and synchronization to hydrate cloud data lakes and data warehouses from various sources.
Data ingestion is the process of moving and replicating data from various sources – databases, files, streaming, Change Data Capture (CDC), applications, IoT, machine logs, etc. – into a landing or raw zone like a cloud data lake or cloud data warehouse where it can be used for business intelligence and downstream transactions for advanced analytics readiness.
A code-free wizard-based approach to data ingestion helps save data engineers extract, transform and load (ETL) effort by efficiently ingesting databases, files, streaming, and applications to better handle the scale and complexity of the business demand of the data.
Data ingestion is the first step of cloud modernization. It moves and replicates source data into a landing or raw zone (e.g., cloud data lake) with minimal transformation. Data ingestion works well with real-time streaming and CDC data, which can be used immediately – with minimal transformation for data replication and streaming analytics use cases. With data ingestion, companies can accelerate the availability of all types of data for driving innovation and growth.
Once the data is ingested into a landing or raw zone, you need to parse, filter, and transform the data to make it available for advanced analytics and AI usage. This is where data integration comes in. It helps to transfer and sync different data types and formats between systems and applications. Data integration is not a one-and-done event, but a continuous process that keeps evolving as business requirements, technologies, and frameworks change.
Enterprises across industries increasingly want to take advantage of the flexibility of multicloud and hybrid cloud offerings to drive data science and analytics practices for competitive advantage. To achieve this goal, they need to surface all the data types to their users via data ingestion – with any pattern and at any latency.
Let’s explore the various use case patterns data ingestion supports:
Cloud data lake ingestion: Data ingestion solutions enable mass ingestion of data sources (e.g., files, databases, applications, streaming, IoT data) into a cloud data lake target (e.g., Amazon Web Services S3 [AWS S3], Google Cloud Storage [GCS], Microsoft Azure Data Lake Storage [ADLS], Microsoft Azure Synapse, Snowflake).
Tip: The speed and quality of the ingestion process correspond with the quality of the cloud data lake. If you ingest your data incorrectly, it can jeopardize the value of the data, resulting in unreliable analytics. Therefore, data ingestion is critical to the success of your cloud data lake implementation for driving AI and machine learning approaches – ultimately improving the accuracy of business predictions and spurring innovation.
Data warehouse modernization/database migration/database synchronization: Data ingestion solutions can help accelerate your data warehouse modernization initiatives by mass ingesting on-prem databases (e.g., Oracle, SQL Server, MySQL), data warehouses (e.g., Teradata, Netezza), and mainframe content into a cloud data warehouse (e.g., Amazon Redshift, Databricks Delta Lake, Google BigQuery, Microsoft Azure Synapse, and Snowflake).
Tip: It helps to synchronize ingested data with change data capture (CDC), which enables continuous incremental replication by identifying and copying data updates as they take place. Data ingestion with CDC capabilities helps you meet today's real-time requirements of modern analytics for faster, more accurate decision-making.
Real-time analytics: Real-time stream processing of events can help unlock new revenue opportunities. For example, real-time processing of customer data can help telcos improve sales and marketing. In addition, tracking devices with IoT sensors can improve operational efficiency, reduce risk, and yield new analytics insights.
Tip: To do real-time analytics, you need to ingest real-time streaming data (e.g., clickstream, IoT, machine logs, social media feeds) into a message hub or streaming targets (e.g., Kafka, Azure Event Hub, Google Pub/Sub) for real-time processing while the events are still happening. This real-time data can help improve the accuracy of AI projects.
Businesses are using different approaches to ingest data from a variety of sources (e.g., traditional databases, data warehouses, mainframe systems, streaming data, machine logs) into cloud data lakes and data warehouses to accelerate their cloud modernization journey. But most businesses are struggling.
Here are the key data ingestion challenges hindering cloud modernization initiatives:
Out-of-the-box connectivity to sources and targets: The diversity of the data makes it difficult to capture 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. It takes too much time and effort to write all that code. Instead, look for prebuilt, 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 incredibly challenging to manually monitor ingestion jobs to detect anomalies in the data and take necessary actions. Instead, be sure to infuse intelligence and automation in your data ingestion process, so you can automatically detect ingestion job failure and execute rules for remedial action.
Manual approaches and hand-coding: The global data ecosystem grows more diverse, and data volume has exploded. Under such circumstances, writing custom code to ingest data and manually creating mappings for extracting, cleaning, and replicating 1000s of database tables can be complex and time-consuming.
Addressing schema drift: One of the biggest challenges of data ingestion is schema drift. Schema drift happens when the schema changes in the source database. If it is not replicated in the target database or data warehouse, it could seriously hamper your workflow.
For example, if you don't address schema drift, data replication can fail, leaving users unable to access real-time data. In addition, data engineers who use hand-coding to build data pipelines must rewrite data ingestion code every time API endpoints or files from your data sources change. This process is time-consuming and unproductive.
Data ingestion, ETL, and ELT methods are used interchangeably to collect, migrate, and transform data from various distinct sources into the cloud data warehouse. However, data ingestion, ETL, and ELT are closely related concepts. But they are not the same thing. So, let’s look at the difference between these three concepts and how data ingestion works with ETL and ELT?
In the ETL approach, you use a third-party tool to extract, transform and load the data into an on-prem or cloud data warehouse to make it available for downstream analytics.
Using an ELT approach, you use the power of the data warehouse database to perform that transformation.
Data ingestion is critical for ETL and ELT processes to extract or ingest structured and unstructured data from various sources and load it into a cloud data warehouse or data lake for further processing. Data ingestion collects, filters, and sanitizes the data at low latency, high throughput, and continual process even when the characteristics of the data change. In addition, it successfully replicates the changes from source to target, making sure the data pipeline is up-to-date.
Data ingestion is a core capability for any modern data architecture. A proper data ingestion infrastructure should allow you to ingest any data at any speed using streaming, file, database, and application ingestion with comprehensive and high-performance connectivity for batch or real-time data.
Below are the seven must-have attributes for any data ingestion tool to future-proof your organization:
With Informatica's comprehensive, cloud-native mass ingestion solution, you get access to a variety of data sources by leveraging more than 10,000 metadata-aware connectors. You can easily access the data to find it and ingest it to where you need it using Cloud Mass Ingestion Files, Cloud Mass Ingestion Streaming, and Cloud Mass Ingestion Application..
Combining that with database and application change data capture services, you can trust you are getting the most up-to-date data for your business priorities.
Benefits of Informatica Cloud Mass Ingestion
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 its manual ETL efforts by 90% and helped its developers build predictive models for advanced analytics to improve student recruitment, admission, and retention. In addition, UNO plans to ingest change data capture into Snowflake, so the latest data from Workday is always available in the warehouse.
SparkCognition captures streaming data to improve machine learning models
Informatica enabled SparkCognition to pursue new AI use cases, such as fraud detection. As data sets grow larger, SparkCognition customers will efficiently bring many data sources into their data science platform, Darwin, using Informatica Cloud Mass Ingestion for predictive analytics and AI/ML usage.
Cloud Mass Ingestion is the unified ingestion capability of the Informatica Intelligent Data Management Cloud. It is designed to ingest any data on any platform and any cloud as well as multicloud and multihybrid environments. It allows you to maintain a federated data warehouse and lake by ingesting data in real time – enabling teams across the business to make data-driven decisions.