Table of Contents
What Are Data Ingestion Tools?
Why Do You Need Data Ingestion Tools?
Data Ingestion Tools vs. Data Integration Tools
How Do Data Ingestion Tools Work?
What Should You Look for In a Data Ingestion Tool?
Data Ingestion Tool Use Cases
Data Ingestion Tools for Real Time Analytics
Benefits of Using a Data Ingestion Tool
Data Ingestion Tools in Action
Conclusion
Resources
What Are Data Ingestion Tools?
Data ingestion is the process of moving and replicating data from data sources to destinations such as a cloud data lake or cloud data warehouse. Data ingestion is the first step in building the enterprise data stack. Tools for data ingestion help to move and replicate data from multiple sources into an endpoint such as a data lake or data warehouse where data is stored and made fit-for-business use through business intelligence and data analytics.
Data sources can come from both on-premises and in the cloud. They are often diverse, including mainframe systems, traditional databases, data warehouses, files, streaming data, change data capture (CDC), applications, IoT and machine logs. Data ingestion should provide versatile connectivity to address the diversity of types of data sources and targets. With data ingestion tools, companies can more easily schedule deployment, real-time monitoring and life-cycle management of ingestion jobs.
See Informatica’s Cloud Mass Ingestion Service in Action
Data ingestion tools like cloud mass ingestion will help you to quickly and easily ingest data from just about any source
Why Do You Need Data Ingestion Tools?
Overview of common enterprise data ingestion challenges.
Given the volume and variety of data and data sources in modern enterprises, faster and more reliable tools are needed to extract data and ingest it into a destination safely. Businesses approach moving data and handling data stores in ways best suited for them. As a result, they often seek the best data ingestion tools to fit their needs. No matter what your specific business requirements, there are many common challenges that data ingestion tools can help to resolve including:
Out-of-the-box connectivity
The diversity of data makes it difficult to capture from various on-premises and cloud sources. This is true no matter what the data volume or data format. Many artificial intelligence (AI) and analytics projects do not succeed as a result. When data capture is neglected, it takes too long to write code. It is also challenging to handle so many individual connectors for multiple data sources. Prebuilt, out-of-the-box connectivity to data sources are preferred. These comprise databases, files, streaming, applications and data flows — including initial and CDC load.
Real-time monitoring and lifecycle management
Companies find it challenging to manually monitor their data processing, data ingestion and replication jobs. It’s also difficult to detect anomalies in the data and take the necessary actions. That’s why intelligence and automation are important to the ingestion and replication process. You can detect ingestion job failure and execute rules for remedial action automatically.
Manual approaches and hand-coding
The global data ecosystem has become more diverse. Because the volume of data has grown, companies can no longer write custom code to ingest and replicate data. It’s simply a thing of the past to manually create mappings to extract, clean and replicate thousands of database tables.
Addressing schema drift
Schema drift is one of the biggest challenges for data ingestion and replication. Schema drift becomes a major issue when schema changes in the source database. If it’s not replicated in the target database or data warehouse, drift can hamper data workflows and data replication can fail. When that happens, users are unable to access real-time data. To build a data pipeline, data engineers must rewrite data ingestion code using hand-coding. Then rewriting code is needed every time there is a change in API endpoints or files from data sources.
Data Ingestion Tools vs. Data Integration Tools
Here’s a brief side-by-side comparison on data ingestion vs. data integration tools.
| Data Ingestion Tools | Data Integration Tools |
|---|---|
| Ingest and replicate data from source to a destination or landing zone. This can be a cloud data lake, data warehouse, or message queue. This is done with the least amount of transformation. | Parse, filter and transform data once ingested. The data is then available for AI and advanced analytics. |
| Consolidate data from a variety of sources into a central repository. From there, the data can be processed even further. | Ensure data is reliable, meets high quality standards and can be used for analytics or reporting. |
| Are less complex and may be implemented without skilled data engineers. | Carry out data massaging and complex transformations, hence may be more complicated and might need skilled data engineers. |
How Do Data Ingestion Tools Work?
There are many ways to ingest and replicate data. Below is a list of ways data ingestion tools can help you depending on your needs:
Change data Capture
CDC is a data integration pattern that allows users to detect and manage small changes at the data source. With CDC, users can apply data changes downstream. This change management can take place across the entire enterprise. CDC manages changes as they happen. With CDC, fewer resources are needed for full data batching. Data consumers can take in changes in real time.
There’s also less impact on the data source or the transit mechanism that links the data source and the data user. The data user only receives the updated data. This saves time, money and resources. CDC propagates these changes onto analytical platforms for real-time, actionable insights. There are several CDC methods with their own advantages and disadvantages, including timestamp CDC, triggers CDC and log-based CDC.
Batch replication
Data engineers can extract data from any source with batch replication. With batch replication, only minimal configurations are needed to load data. Batch replication saves time during data preparation. Large amounts of data can be moved into the cloud. The data is analyzed quickly for business insights. But incremental changes to the source database or data warehouse are not captured. Batch replication is ideal for processing large volumes of data with minimal configurations.
Streaming data replication
Streaming data replication lets you continuously copy streaming data. It works with real-time sources, platforms and hubs including:
- Internet of Things (IoT)
- Sensors
- Social media feeds
- Azure event message hub
- Google pub/sub message hub
- Message hubs like Kafka
Full-table replication
Full-table replication lets you work with all the rows in a table. New, updated or existing rows can be fully replicated. This happens during every job that is earmarked for replication. Full-table replication is a good fit when incremental replication is not possible, such as when records are deleted from the source. Limits of full-table replication include:
- Data latency
- Increased row consumption
- Unavailability of some integration patterns
Snapshot replication
Snapshot replication copies data changes from one database to another. This happens at specific times and on demand. Snapshot replication is helpful when the database is less critical. It is also helpful when the database does not change often.
Asynchronous replication
Asynchronous replication is a type of data storage backup. It is helpful when data is not backed up right after the main storage replication is completed. Instead, the data is backed up over time.
What Should You Look for In a Data Ingestion Tool?
Data ingestion is a core capability for any modern data architecture. An ideal data ingestion tool should allow you to ingest any data at any speed. It should use streaming, file, database and application ingestion with comprehensive and high-performance connectivity for batch processing or real-time data.
Essential capabilities of modern data ingestion tools.
Below are seven must-have attributes for any data ingestion tool to future-proof your organization:
1. Unified experience for data ingestion
Enterprise data is spread across disparate entities. So, you need a single, unified solution to ingest data from many sources. As data is ingested from remote systems, look for an ingestion solution that can apply simple transformations on the data (e.g., filtering bad records) at the edge. This should be done before it is ingested into the lake.
2. Ability to handle unstructured data and schema drift
Many sources emit data in an unstructured form. So, be sure to parse the unstructured data to discover and understand the structure for downstream use. Changes in the structure at the source — often referred to as schema drift — are a key pain point for many organizations. Look for a solution that handles schema drift intelligently. And one that automatically propagates changes to the target systems.
3. Versatile out-of-the-box connectivity
The unified data ingestion solution should offer out-of-the-box connectivity to various sources. This includes files, databases, mainframes, IoT, applications and other streaming sources. Also, it needs to have the capability to persist the enriched data onto various cloud data lakes, data warehouses and messaging systems.
4. High performance
A data-driven culture can succeed only if the data is continuously available. 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 processing using a Lambda architecture. In addition, you can seek out a data ingestion solution that provides recovery from ingestion job failure. It should have high availability and guarantee exactly one delivery for replication use cases.
5. Wizard-based data ingestion
You need to be able to ingest data in an efficient way with a wizard-based tool that requires no hand coding. The data should go into a cloud data warehouse with CDC capability. This will ensure you have the most current, consistent data for analytics.
6. Real-time data ingestion
It’s necessary to accelerate the ingestion of real-time log, CDC and clickstream data into Apache Kafka, Microsoft Azure Event Hub, Amazon Kinesis and Google Cloud Pub/Sub. This enables real-time analytics.
7. Cost-efficient
Well-designed data ingestion should save your company money by automating processes that are currently costly and time-consuming. In addition, data ingestion can be significantly cheaper if your company isn't paying for the infrastructure or skilled technical resources to support it.
Use Cases for Data Ingestion Tools
No matter what industry they are in, modern enterprises are leveraging multi-cloud and hybrid-cloud architectures. Driving effective processes for data analytics practices in these environments gives companies a competitive advantage. The quality of the cloud data lake directly impacts the efficiency of the data ingestion and replication process and the accuracy of business predictions. Here are several use cases supported by data ingestion tools:
Cloud data lake ingestion
Data ingestion tools enable mass ingestion of data sources into a cloud data lake target. Data sources include files, databases, applications, streaming and IoT data. Cloud data lake targets include Amazon Web Services S3 (AWS S3), Google Cloud Storage (GCS), Microsoft Azure Data Lake Storage (ADLS), Microsoft Azure Synapse and Snowflake.
Cloud modernization
Legacy data can negatively impact a company’s cloud modernization journey. This prevents them from tapping into the power of AI and data analytics. Legacy also adds to disparate data sources, as well as data volume, velocity and silos. Data ingestion and synchronization are also greatly impacted, diminishing the ability to move data and hydrate cloud data lakes and data warehouses from multiple sources.
Code-free wizard-based data ingestion helps data engineers save time managing ETL by efficiently ingesting databases, files, streaming data and applications. The scale and complexity of business demands related to data are better handled.
Data ingestion tools can help accelerate your data warehouse modernization initiatives. They do this by mass ingesting:
- On-premises databases such as Oracle, SQL Server and MySQL
- Data warehouses such as Teradata and Netezza
- Mainframe content into a cloud data warehouses including Amazon Redshift, Databricks Delta Lake, Google BigQuery, Microsoft Azure, Oracle Cloud and Snowflake Cloud)
Data ingestion tools help synchronize ingested data with CDC for continuous incremental data replication. This helps meet today's real-time requirements for modern analytics and faster, more accurate decision-making.
Accelerate real-time analytics
Real-time analytics are key to modern data management. When you can process event streams in real-time, you can unlock new revenue opportunities. For instance, telecommunications companies that can process customer data in real time can improve sales and marketing results. Using sensors on tracking devices can improve operational efficiency, reduce risk and yield new analytics insights. To do real-time analytics, ingest real-time streaming data from clickstream, IoT, machine logs or social media feeds into message hubs or streaming targets such as Kafka, Azure Event Hub and Google Pub/Sub.
Benefits of Using a Data Ingestion Tool
Across industries, all-sized organizations employ data ingestion and replication reaping its rewards, which include:
Disaster recovery
Data ingestion tools supports disaster recovery. They preserve a reliable backup of primary data on a non-production database. As a result, data is immediately available for situations including data recovery and failure. Data replication reduces the cost and complexity of protecting critical workloads.
Data availability
Data ingestion tools deliver dynamic, near real-time transactional ingestion and replication. This lets enterprises make accurate business decisions and respond to business events as they happen.
Speed of data access
Data ingestion and replication makes data access faster. It works well in organizations with multiple locations. Users in Asia or Europe may experience latency when reading data in North American data centers. Bringing a data replica closer to the user improves access times and balances the network load.
IT costs
Data replication tools can reduce the IT labor involved in creating and managing data replication transactions across the enterprise. This saves time, money and resources.
Accelerate data integration
Companies are collecting more data than ever. They are struggling to bring together data from various siloed databases and data warehouses and for handling distributed data. They also struggle to deliver actionable analytics and AI. With data ingestion tools, organizations can efficiently ingest and replicate data for cleansing, parsing, filtering and transforming the data. This allows them to make their data available to data users for analytics and AI consumption.
Data Ingestion Tools in Action
Informatica’s end-to-end Intelligent Data Management Cloud™ (IDMC) is the industry's first and most comprehensive AI-driven data management solution. Powered by Informatica’s AI-based CLAIRE® engine, IDMC applies industry-leading metadata capabilities with data replication and data ingestion services to accelerate and automate core data management processes. Here are some examples of how IDMC data ingestion services are helping customers advance their data management capabilities:
University of New Orleans (UNO) increases student enrollment and improves retention
See how the University of New Orleans is increasing data resiliency for hundreds of gigabytes with data replication to Snowflake.
Using Informatica IDMC cloud mass ingestion services, UNO accelerated their cloud modernization journey. They quickly and efficiently migrated thousands of tables with complex data structures from Oracle to Snowflake. And they did it without any hand-coding.
The easy-to-use wizard-based approach helped UNO significantly reduce their manual ETL efforts by 90%. It also helped their developers build predictive models for advanced analytics. This effort helps to improve student recruitment, admission and retention. UNO plans to ingest CDC data into Snowflake, so, the latest data from Workday is continuously available in their data warehouse.
KLA moves 12 years of data to the cloud in one weekend
KLA wanted to better service its expanding customer base and satisfy internal demand for analytics. So, they partnered with Informatica and Snowflake to accelerate a cloud-first data strategy. The strategy helped KLA expedite critical reports and enable better-informed decision-making across many core business teams.
Over a single weekend, they moved approximately 1,000 Oracle database tables into Snowflake. These represented 12 years of historical ERP data. KLA combined multiple data sources in the cloud for analysis. As a result, KLA supports more detailed and more user-friendly reporting. Now, their teams can predict demand across complex and often customized product groups.
Finance company combats fraud with real-time streaming analytics
A leading global financial company used data replication to better detect and prevent an increasing number of fraudulent transactions. They needed to be able to send real-time alerts to their customers about fraudulent transactions.
Through data ingestion, they put a CDC sense-reason-act framework to work. They ingested transaction information from their database. A fraud detection machine learning (ML) model detected potentially fraudulent transactions, and then the company alerted customers. Real-time streaming analytics data delivered out-of-the-box connectivity. Their dream of end-to-end data ingestion and streaming became a reality.
Conclusion
Companies with large volumes and varieties of data need high data availability. Data ingestion and replication for big data enterprises work across data silos. This means that data copies remain the same as their sources. You can transfer data copies into any cloud data lake, data warehouse or database. It’s essential to find an automated and intelligent data replication solution to speed up and simplify the data replication process while keeping data safe.
Resources
For today’s complex data centers, data ingestion and replication are critical for data modernization. With Informatica’s end-to-end, AI-powered IDMC, which includes data replication and ingestion services, you can quickly and safely replicate large volumes of data from a variety of sources. These sources include databases, applications, streaming and files onto cloud or on-premises repositories or onto messaging hubs for real-time analytics. And with Informatica Processing Unit (IPU) consumption-based pricing, you can quickly and easily onboard new IDMC capabilities as you need them.
Learn more about Informatica’s data ingestion and replication solutions.
