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How Your Data Ingestion Framework Turns Strategy into Action

By Posted in Engineering March 21, 2022

With Data Infrastructure predicted to grow to over 175 zettabytes (ZB) by 2025, the debate amongst data engineers is no longer how big the data they encounter will be. Instead, they talk about how best to design a data ingestion framework that ensures that the right data is processed and cleansed for applications that need them

Data ingestion is the first step in the data pipeline. It’s where data is obtained or imported and plays a vital role in the analytics architecture. However, it can be a complex process that requires a proper strategy to ensure that data is properly handled. Data ingestion is supported by the data ingestion framework.

What is a Data Ingestion Framework?

A data ingestion framework is the collection of processes and technologies used to extract and load data for the data ingestion process, including data repositories, data integration software, and data processing tools. 

Data ingestion frameworks are generally divided between batch and real-time architectures. It is also helpful to think of the intent of the end-user application: whether you will use the data pipeline to make analytical decisions for the business or as part of a data-driven product. 

How Frameworks Put Your Data Ingestion Strategy to Work

In software development, a framework is a conceptual platform for application development. Frameworks provide a foundation for programming in addition to tools, functions, generic structure, and classes that help streamline the application development process. In this case, your data ingestion framework simplifies the data integration and collection process from different data sources and data types. 

The data ingestion framework you choose will depend on your data processing requirements and its purpose. You have the option to hand-code a customized framework to fulfill the specific needs of your organization, or you can use a data ingestion tool. Since your data ingestion strategy informs your framework, some factors to consider are 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. Once you’ve determined your data ingestion strategy, you can move on to the data ingestion process flow.  

Components of a Data Ingestion Process Flow

All data originates from specific source systems and, depending upon the type of source, will subsequently be routed through different steps in the data ingestion process. Source systems broadly consist of OLTP databases, cloud, and on-premises applications, messages from Customer Data Platforms, logs, webhooks from third-party APIs, files, and object storage. 

Source systems need to be orchestrated through a series of workflows or streamed across the data infrastructure stack to their target destinations because data pipelines still contain a lot of custom scripts and logic that don’t fit perfectly into a regular ETL workflow. Workflow orchestration tools like Airflow perform this function by scheduling jobs across different nodes using a series of Directed Acyclic Graphs (DAGs).

Next, metadata management comes in early in this process so that data scientists can do data discovery downstream and tackle issues such as data quality rule definitions, data lineage, and access control groups. 

Once the necessary transformations are complete, this data’s “landing” zone can be a data lake like Apache Iceberg, Apache Hudi, or Delta Lake, or a cloud data warehouse like Snowflake, Google BigQuery, or Amazon Redshift. We often use data quality testing tools to check for issues like null values, renamed columns, or checkpointing certain acceptance criteria. Depending on the use case, data is also orchestrated after being cleaned from a data lake to a data warehouse. 

This is the point where – based on the specific use case (analytical decisions or operational data feeding into an application) – data can be sent to a data science platform. Platforms may include Databricks or Domino Data Labs for machine learning workloads, be pulled by an ad-hoc query engine like Presto or Dremio, or used for real-time analytics by Imply, Clickhouse, or Rockset. Then, as the last step, analytics data is sent to dashboards like Looker or Tableau, while operational data is sent to custom apps or application frameworks like Streamlit.    

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Deciding Between Batch and Streaming Data Ingestion

Choosing between batch and streaming data ingestion depends very much upon whether the data is to be used for analytical decisions or operationally in a data-driven product. Given that there are data sources that are used for both purposes, it is imperative that streaming data ingestion be treated on par with batch data ingestion. For example, OLTP databases, Customer Data Platforms, and logs emit a continuous stream of data that has to first be ingested by an event streaming framework like Apache Kafka or Apache Pulsar, and then for further stream processing prior to being sent to a data lake

To do this properly, separation of concerns between analytical and operational workloads has to be configured so that objectives for analytical workloads (such as correctness and predictability) versus the objectives for operational workloads (such as cost-effectiveness, latency, and availability) can be met. 

Common Challenges Encountered with Data Ingestion

There are several challenges that data engineers can encounter with data ingestion, especially when it comes to real-time. Schema drift, latency issues, and metadata management roadblocks are only a few of the problems. And given that metadata management and schema drift apply to batch data ingestion as well, it is imperative that the right streaming platform be used early on in the data ingestion process, at the orchestration layer itself.

Scaling Data Ingestion Processes: Real-time is Key

Given that there are many different types of data pipelines that are fed by data ingestion and that real-time data ingestion plays a huge role in addressing some of the challenges mentioned above, it is important to implement the right architecture to scale real-time data ingestion processes. 

Allowing users to query data at a high level without worrying about schema issues from heterogeneous data sources, query optimizations within the ad-hoc layer or real-time analytics engines, and data processing logic throughout the workflow is an absolute necessity.

Query performance can degrade due to spikes from end-user applications and this can often be solved by sharding the data so that the Queries per Second (QPS) thresholds can be met.

Metadata management issues can be resolved using real-time data ingestion by implementing a change log that can provide a view into the entire history of how data has been appended, modified, or transformed. 

Latency has many factors to it, and when it comes to one of the most widely adopted frameworks like Apache Kafka, data engineers want to reduce end-to-end latency from the time a producer writes data to Apache Kafka to when a consumer reads data from it. The number of partitions, replications, and brokers plays a large part in this endeavor. 

Mapping Sources of Data to Targets of Data

Data ingestion frameworks will change depending on your data sources and targets. Data warehouses, like Amazon Redshift, contain structured data that likely has defined relationships and often require data ingested into them to be in a certain format. For example, if you try to send data to a warehouse that doesn’t match your destination table schema or violates a constraint that has been added to that table you’re going to get an error. Data lakes like AWS S3 are a lot less picky and can usually receive any type or format of data – structured, semi-structured, or unstructured. 

Techniques Used to Ingest Data

Data ingestion involves different techniques and software languages used to code data ingestion engines. For starters, extract/transform/load (ETL) and extract/load/transform (ELT) are two integration methods that are quite similar. Each method enables data movement from a source to a data warehouse. The key difference is where the data is transformed and how much of the data is retained in the warehouse. 

ETL is a traditional integration approach that involves transforming data for use before it’s loaded into the warehouse. Information is pulled from remote sources, converted into the necessary styles and formats, and is then loaded into its destination. However, with the ELT method, data is extracted from one or multiple remote sources and loaded directly into its destination without any formatting. Data transformation takes place within the target database.

Several programming languages are used to code data ingestion engines when manipulating and analyzing big data. Some of the most popular languages are:

  • Python is considered one of the fastest-growing programming languages and is used across a broad spectrum of use-cases. It’s known for its ease of use, versatility, and power.
  • Once the go-to cross-platform programming language for complex applications, Java is a general-purpose programming language used across various applications and development environments.
  • Scala is a fast and robust language that many professionals in big data use. 

As organizations take a data-driven approach to make business decisions, several variables inform the data ingestion process. A metadata-driven ingestion framework is one of them. This approach skips the lengthy loading and integration processes by relying on metadata. As a result, it’s been instrumental in powering big data analytics and business intelligence––helping inform decisions regarding customers, business operations, and business processes. Additionally, automation has improved data ingestion by making it easier, faster, and more scalable. 

Streamlining Data Ingestion with Smart Data Pipelines

As your organization looks to grow and gain a competitive advantage in real-time decision-making, StreamSets DataOps Platform can provide you with the resources you need to get there. Our end-to-end data engineering platform delivers continuous data to your organization to support the data ingestion process.

With our platform, you can quickly build and automate data pipelines while minimizing the usual ramp-up time needed to implement new technologies. Contact us today to start building smart data pipelines for data ingestion.

 

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