Metadata Driven Data Ingestion: Simplified 101

The process of obtaining and importing data for immediate use or storage in a database is known as Data Ingestion. Taking something in or absorbing something is referred to as ingesting.

Data can be ingested in batches or streamed in real time. Each data item is imported as the source emits it in real-time data ingestion. When data is ingested in batches, discrete chunks of data are imported at regular intervals of time. Prioritizing data sources is the first step in a successful Metadata driven data Ingestion process. Individual files must be validated, and data items must be routed to the right places.

When there are hundreds of different Big Data sources in various formats, the sources may number in the hundreds, and the formats may number in the dozens. It is difficult to ingest data at a reasonable speed and process it efficiently in this situation. Vendors provide software that automates the process and customizes it for specific computing environments and applications.

Data preparation capabilities may be included in the software used when Data Ingestion is automated. These functions structure and organize data so that it can be analyzed immediately or later using Business Intelligence (BI) and Business Analytics software. Data can come from a variety of places, including Data Lakes, IoT devices, on-premises databases, and SaaS apps, and it can end up in a variety of places, including cloud Data Warehouses and Data Marts.

Data Ingestion is a vital technology that enables businesses to make sense of the ever-increasing volumes and complexity of data. We’ll delve deeper into this technology to help businesses get more value from Data Ingestion.

Data Ingestion enables teams to work more quickly. The scope of any given Data Pipeline is purposefully limited, allowing data teams to be flexible and agile at scale. Data analysts and data scientists can easily build a single data pipeline to move data to their preferred system once the parameters are set. The following are some examples of data ingestion:

• Transfer data from Salesforce.com to a data warehouse, then use Tableau to analyze it.
• Data from a Twitter feed can be captured for real-time Sentiment Analysis.
• Obtain data for Machine Learning model training and experimentation.

Data Ingestion pipelines are used by data engineers to better handle the scale and complexity of data demands from businesses. Having a large number of intent-driven data pipelines running in the background without the involvement of a development team allows for unprecedented scale in achieving important business goals. Among them are:

• Microservices can be used to speed up payments for a global network of healthcare providers.
• With a self-service data platform, you can support AI innovations and business cases.
• In a customer 360 Data Lake, detect fraud with real-time ingestion and processing.

For analysts and data scientists to access data for real-time analytics, Machine Learning, and AI workloads, Data Ingestion has become a crucial component of self-service platforms.

What is Metadata?

Metadata is information about information. Metadata adds information to the data, making it easier to find, use, and manage.

Metadata comes in a variety of forms, each with its purpose, format, quality, and volume. The following are some of the most common types of metadata: descriptive, structural, administrative, and statistical.

One example of metadata is the information written on a letter envelope to assist in the delivery of the actual content – the letter – to its intended recipient. HTML tags, for example, tell web browsers how to layout pages so that humans can read them and follow links to other pages more easily.

Through references to concepts formally described in a Knowledge Graph, semantic metadata aids computers in interpreting the meaning of data. 

Metadata describes objects and adds more granularity to the way they are represented, similar to how library cards describe books. 

• Descriptive metadata includes information about who created a resource, as well as what it is about and what it contains. This is best accomplished through the use of semantic annotation.
• Additional data about the way data elements are organized – their relationships and the structure they exist in – is included in structural metadata.
• Administrative metadata contains information about the origin, type, and access rights of resources.

Metadata is the foundation of all digital objects and is essential to their management, organization, and use.

Metadata, when properly created and managed, contributes to information clarity and consistency. Metadata makes it easier to find relevant information and to search for and retrieve resources. Any digital object that has been tagged with metadata can be automatically associated with other relevant elements, making it easier to organize and discover. This allows users to connect with people they might not have met otherwise.

You can use metadata to:

• Search for resources using a variety of criteria; identify different resources;
• Gather resources by topic and track them down.

Understanding Metadata Driven Data Ingestion 

With data infrastructure expected to reach over 175 zettabytes (ZB) by 2025, data engineers are debating how big the data they will encounter will be. Instead, they should discuss how to create a Data Ingestion framework that ensures the correct data is processed and cleansed for the applications that require it.

Data Ingestion Framework

The first step in the data pipeline is Data Ingestion. It is the location where data is obtained or imported, and it is an important part of the analytics architecture. However, it can be a complicated process that necessitates a well-thought-out strategy to ensure that data is handled correctly. The Data Ingestion framework helps with data ingestion.

A Data Ingestion framework consists of the processes and technologies that are used to extract and load data for the Data Ingestion process, such as data repositories, data integration software, and data processing tools.

Batch and real-time Data Ingestion architectures are the most common. Consider the end-user application’s purpose: whether the data pipeline will be used to make business-critical analytical decisions or as part of a data-driven product.

A framework is a conceptual platform for application development in software development. Frameworks provide a programming foundation as well as tools, functions, generic structure, and classes that aid in the application development process. In this case, your Data Ingestion framework makes the process of integrating and collecting data from various data sources and types easier.

The Data Ingestion framework you select will be determined by your data processing needs and intended use. You can either hand-code a customized framework to meet your organization’s specific needs, or you can use a Data Ingestion tool. The complexity of the data, whether or not the process can be automated, how quickly it’s needed for analysis, the regulatory and compliance requirements involved, and the quality parameters are all factors to consider because your Data Ingestion strategy informs your framework. You can move on to the Data Ingestion process flow once you’ve decided on your Data Ingestion strategy.

Components of Data Ingestion

All data comes from specific source systems and is then routed through various steps in the Data Ingestion process, depending on the type of source. OLTP databases, cloud and on-premises applications, messages from Customer Data Platforms, logs, webhooks from third-party APIs, files, and object storage are all examples of source systems.

Because data pipelines still contain a lot of custom scripts and logic that don’t fit perfectly into a regular ETL workflow, they must be orchestrated through a series of workflows or streamed across the data infrastructure stack to their target destinations. Airflow and other workflow orchestration tools accomplish this by using a series of Directed Acyclic Graphs to schedule jobs across multiple nodes (DAGs).

Then, metadata management is introduced early in the process so that data scientists can do downstream data discovery and address issues like data quality rule definitions, data lineage, and access control groups.

This data’s “Landing” zone can be a data lake, such as Apache Iceberg, Apache Hudi, or Delta Lake, or a cloud data warehouse, such as Snowflake, Google BigQuery, or Amazon Redshift, once the necessary transformations have been completed. Data Quality Testing tools are frequently used to check for issues such as null values, renamed columns, and checkpointing certain acceptance criteria. Data is also orchestrated after it has been cleaned and moved from a data lake to a data warehouse, depending on the use case.

This is the point at which data can be sent to a data science platform based on the specific use case (analytical decisions or operational data feeding into an application). Databricks or Domino Data Labs for machine learning workloads, Presto or Dremio for ad-hoc query engines, and Imply, Clickhouse, or Rockset for real-time analytics are examples of platforms. The analytics data is then sent to dashboards like Looker or Tableau, while operational data is sent to custom apps or application frameworks like Streamlit as the final step.

If the data will be used for analytical decisions or operationally in a data-driven product, the choice between batch and streaming Data Ingestion is critical. Because data sources are used for both, streaming Data Ingestion must be treated equally with batch data ingestion. OLTP databases, Customer Data Platforms, and logs, for example, emit a continuous stream of data that must first be ingested by an event streaming framework such as Apache Kafka or Apache Pulsar and then processed before being sent to a data lake.

The separation of concerns between analytical and operational workloads must be configured properly to ensure that analytical workload objectives (such as correctness and predictability) are met while operational workload objectives (such as cost-effectiveness, latency, and availability) are met.

Data Ingestion Challenges

When it comes to real-time data ingestion, data engineers face several challenges. Only a few of the issues include schema drift, latency issues, and metadata management roadblocks. Given that metadata management and schema drift also apply to batch data ingestion, it’s critical to start using the right streaming platform at the orchestration layer.

Scaling Data Ingestion Processes

Given the variety of data pipelines that are fed by Data Ingestion and the importance of real-time Data Ingestion in addressing some of the issues mentioned above, it’s critical to implement the right architecture to scale real-time data ingestion.

Allowing users to query data at a high level without having to worry about schema issues from heterogeneous data sources, query optimizations in the ad-hoc layer or real-time analytics engines, and data processing logic throughout the workflow are all essential.

Due to spikes from end-user applications, query performance can suffer, but this can usually be remedied by sharding the data to meet the Queries per Second (QPS) thresholds.

Implementing a changelog that can provide a view into the entire history of how data has been appended, modified, or transformed can help to resolve metadata management issues when using real-time data ingestion.

When it comes to one of the most widely used frameworks, such as Apache Kafka, data engineers want to reduce end-to-end latency from the time a producer writes data to it until a consumer reads it. In this endeavor, the number of partitions, replications, and brokers is critical.

Mapping Sources to Targets of Data

Depending on your data sources and targets, your Data Ingestion framework will evolve. Data warehouses, such as Amazon Redshift, hold structured data with known relationships, and they frequently require data to be entered in a specific format. You’ll get an error if you try to send data to a warehouse that doesn’t match the schema of your destination table or violates a constraint added to that table. Data lakes, such as AWS S3, are less picky, and can typically accept any type or format of data, whether structured, semi-structured, or unstructured.

Techniques to Ingest Data

Data Ingestion engines are coded using a variety of techniques and software languages. To begin, ETL and ELT are two very similar integration methods. Data can be moved from a source to a data warehouse using any of these methods. Where the data is transformed and how much of it is retained in the warehouse are the two main differences.

ETL is a traditional integration method that entails preparing data for use before loading it into a warehouse. Data is gathered from remote sources, converted into the appropriate styles and formats, and then loaded into its final destination. The ELT method, on the other hand, extracts data from one or more remote sources and loads it unformatted into its destination. The target database is where the data transformation occurs.

When manipulating and analyzing big data, Data Ingestion engines are coded in a variety of programming languages. The following are some of the most widely used languages:

• Python is one of the fastest-growing programming languages, with applications in a wide range of fields. It’s well-known for its simplicity, adaptability, and power.
• Java is a general-purpose programming language that is used across various applications and development environments. It was once the go-to cross-platform programming language for complex applications. Many big data professionals use Scala, which is a fast and reliable language.

Several variables influence the Data Ingestion process as businesses adopt a data-driven approach to decision-making. One of them is a metadata-driven ingestion framework. By relying on metadata, this approach avoids the time-consuming loading and integration processes. As a result, it’s been critical in enabling Big Data Analytics and Business Intelligence––helping to inform customers, business operations, and business process decisions. Automation has also improved Data Ingestion by making it simpler, faster, and more scalable.

Metadata-Driven ETL 

Declarative programming, as opposed to Object-Oriented and Procedural Programming, is used in metadata-based ETL. Declarative distinguishes between “what should be done” and “how to do it.” Metadata defines “what should be done,” similar to a data dictionary that defines data mappings, data models, data types, data transformations, and so on. The “how to do it” is implemented in pre-built code that employs ETL functionality encapsulation.

If the metadata is as complex as the coding it is replacing, a declarative metadata solution is useless. Fortunately, ETL metadata strikes a good balance between minimalism and simplicity while still meeting business and technical needs. The metadata can be represented in an easy-to-read and understand data dictionary format. It is formatted in the same way as ETL technical requirements documents.

Metadata that is declared ETL can respond in real-time to source schema changes from SQL or NoSQL databases. The data dictionary is updated as a result of these changes. Optionally, the schema change’s DDL can be applied to a SQL destination based on the data model’s rules. Another important concept in Metadata ETL is loose binding to a schema, which is demonstrated by this feature. Most ETL vendors use strict binding, which means that any changes to the source or destination schema will result in the package failing. Changes to a schema rarely cause metadata ETL to fail.

For internal processing, metadata ETL typically uses JavaScript Object Notation (JSON) documents, which support hierarchical data and are ideal for SQL and NoSQL integration. The rules-based ETL allows for automated NoSQL to SQL conversion, which normalizes hierarchical data into a tabular format. This method can be used to connect NoSQL to a data warehouse or reporting database. Metadata ETL works with any database format, including SQL, NoSQL, and hybrids of the two.

Bulk/ad hoc metadata manipulation is another value proposition for metadata ETL. The entire database schema, as well as the data model, can be more effectively validated and manipulated. Changes between environments or systems can be quickly detected using simple data comparison techniques. With the ability to search metadata, ETL changes can be quickly implemented and edited without having to manually traverse the ETL code.

Metadata-based ETL Use Case

Examining a case study of a metadata ETL early adopter can provide some practical examples of the approach’s utility. 

• The Equator is a mortgage banking industry software as a service (SaaS) provider. Three of the four largest banks in the United States are among their clients. Complex data must be fed to clients in a nightly batch mode by business operations. Multiple normalized ODS style databases are populated by the OLTP source systems, which are a hybrid of relational and semi-structured data. 
• Across all environments and clients, they have approximately one million data attributes in 5,000 tables under ETL management. 
• Equator’s major customer requirement is client data integration. Customer acquisition and retention are driven by integrating data back to clients as painlessly as possible in a SaaS business model. 
• Most Equator clients’ ETL teams prefer that the semi-structured OLTP data be normalized. Equator struggled to implement basic metadata-controlled ETL using a traditional ETL tool that required a lot of SQL coding. It was extremely brittle, and it took at least 6 hours to complete. 
• They implemented a true Java-based metadata ETL engine, which significantly improved operations and reduced run times to one hour.
• Streamlining the integration between application development, OLTP schema management, and data integration could be one of the additional benefits of metadata-based ETL. Application, database, and ETL teams could collaborate on these activities by sharing a central repository of data identities. 
• For schema metadata, this concept is based on a master data management strategy. Because the OLTP database cannot enforce data types on semi-structured data, enabling this capability will eliminate the final major challenge of application data entry: the need for consistency in expected data values with ETL.

To effectively manage metadata, leveraging a data dictionary tool can help document and standardize metadata for your organization

Advantages of Metadata Driven Data Ingestion

• Uniformity: The Metadata Driven Framework approach results in a standardized, generic Data Ingestion process. Understanding the ingestion pattern makes it very simple to review existing configurations or add new configurations.
• Agility: This Framework approach gives you a lot of flexibility when it comes to creating and changing configurations. Any changes to ingestion would primarily involve changing the DMLs for meta-data without requiring any code changes, which is critical for an agile methodology.
• Easy to Scale: The ease with which new sources, configurations, environments, and other items can be added simply by creating meta-data demonstrates the ease with which the system can scale. 
• Maintainability: This approach is very easy to maintain because everything from business logic to data flow is in the form of excel documents.
• Acceleration: ETL frameworks don’t have to be used in place of existing ETL platforms. It could be useful as a code generator or accelerator for rapid development in the native ETL platform. The Framework, for example, can be used to create custom XML factory templates that can be imported into Informatica custom repositories to generate ready-to-use ETL.

Conclusion

This blog discusses critical aspects of Metadata Driven Data Ingestion. In addition to that, it describes Data Ingestion and Metadata.

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