Work with data, and you will hear a commonly used term -- data ingestion. So, what is data ingestion, and how does it help process big data?
Data ingestion is the process of moving or transporting data from one or more sources to a destination platform for further processing and analysis. The data can be sourced from various sources, including databases, data lakes, SaaS apps, IoT devices, and so on.
Data ingestion is the first step in implementing a data pipeline. As the name indicates, data ingestion involves importing data from various sources into a database or data storage system.
Data can be validated during the data ingestion process to ensure it meets industry standards. It can also be transformed to ensure compatibility with the destination. The entire process can also be tweaked to ensure the real-time handling of errors. All these factors during data ingestion ensure that the data is accurate, consistent, and reliable.
Data ingestion improves data quality by enhancing accuracy, ensuring data completeness, verifying data formatting, eliminating data redundancy, and increasing data accessibility.
Automated data ingestion facilitates the easy movement of data, even for non-technical employees. They can use an ETL tool to add data sources and select a destination for storage and further processing without requiring any high-end technical skills. This frees your workforce for more productive jobs while automated tools handle data profiling and cleansing.
Data ingestion assists you with business intelligence. Businesses can better comprehend the history behind certain data trends and use them to predict future trends.
Data ingestion helps businesses better understand the audience's needs, thus allowing them to pivot themselves as per the demand. This also helps key decision-makers make well-informed decisions, improve customer service, and create high-quality products.
Data ingestion consists of various steps -- data identification and selection, data collection, data preparation, data integration, data validation and testing, and data governance and security.
Skipping any of these steps can pose several risks, including:
Important data ingestion techniques like caching and batch processing keep API calls in check. Skipping these will require you to make frequent API calls, thus increasing API usage, burdening the server, and increasing costs.
If all the steps of data ingestion are not followed correctly, the data structure could change over time, leading to schema drift. It will then become a problem to maintain data quality and consistency, thus adversely impacting your analytics projects and costs.
As already seen above, excessive API usage and schema drift adds to costs. Further, if data ingestion steps like deduplication and data cleansing are skipped, you will end up with poor-quality data. Storing duplicate records can increase storage costs. And cleansing the data later in the pipeline can impact the operations, waste time, and create chaos.
Skipping data ingestion steps can impact your data analytics projects. If you don't clean your data, for example, you may end up with inaccurate or incomplete data, which can negatively impact your analytics results.
ETL stands for extract, transform, and load. Data ingestion takes care of the first step of the ETL process -- extract.
Meanwhile, data ingestion extracts data from different sources and loads it into a centralized data storage system.
Data ingestion helps data engineers to fetch data from data sources for further processing. Once the data is extracted, it can be further integrated, transformed, and processed to analyze and derive insights to drive critical business-making processes.
Related Read: Data Ingestion vs. ETL: Key Differences
Data ingestion works with all kinds of data sources:
These include data sources with a well-defined structure, typically stored in a database or a flat-file format. Some examples include relational databases like SQL and Oracle, flat file formats like CSV and TSV files, and APIs that return data in a structured format like XML and JSON.
Unstructured data sources don't have a defined or organized structure. This could include text documents like PDFs, web pages, emails and reports, images, audio and video files, and social media posts.
Real-time data is produced in real-time. Think data extracted through social media, streaming data media, financial market, customer interactions, and so on.
Batch datasets are the opposite of real-time data. This data type is processed in discrete batches. It is commonly used in industries like marketing, healthcare, and finance, where historical data is studied to derive insights for decision-making.
Data can also be ingested from various sources, including IoT devices, social media platforms, and other data processing systems like Hadoop.
Data ingestion can be performed through three approaches:
Real-time data ingestion involves moving data from source systems to destination platforms in real time.
Real-time streaming is useful when time is of utmost importance, like stock market trading or power grid monitoring.
Real-time data ingestion enables organizations to respond immediately to new information and make critical business decisions based on new insights.
Batch-based data ingestion, contrary to real-time data ingestion, doesn't happen in real-time but in batches according to certain triggers.
Data ingestion can be triggered based on set schedules or as a trigger to any external event or other logical ordering.
Lambda architecture-based data ingestion provides the right mix of both real-time and batch methods.
Lambda architecture consists of three layers -- batch, serving, and speed layers.
The batch and serving layers index data in batches, while the speed layer indexes data that is yet to be picked by the slower first two layers.
This mix of slow and instant layers ensures data is queried with minimal latency.
Thus, it is highly recommended to develop the right data management strategy, consisting of the batch, real-time streaming, or a mix of both, depending on your needs and preferences.
Understanding the data ingestion pipeline components accurately is highly recommended to comprehend how it functions.
A typical data pipeline consists of seven points:
Origin: This is the origin point of a data pipeline. It most commonly comprises data warehouses, social media, IoT device sensors, data lakes, and social media.
Destination: Destination is where the data is finally transported to. Depending on your use case, it could be a data warehouse or data lake.
Dataflow: Dataflow determines the flow of the data. This means how data moves through a pipeline. Typically, it involves ETL (extract, transform, and load) or ELT (extract, load, and transport) operations.
Storage: Storage determines all the systems that contribute to data movement. Storage options are generally determined by the volume of data, usage of data, and the frequency at which the storage system is searched.
Processing: Next up, processing determines all the steps and activities required to process the data. It is essential to process the data depending on its use cases.
Workflow: The workflow determines the jobs and how they function upstream and downstream. The upstream and downstream refer to the data movement as it moves via a pipeline from various sources and destinations.
Monitoring: Monitoring determines data's efficiency, consistency, and accuracy as it moves through different processing stages.
To understand how your pipeline functions and ensure smooth data flow, it is important to understand these crucial components and align them as per your objectives.
Today, cloud data warehouses like Snowflake, Amazon Redshift, Microsoft Azure SQL Data Warehouse, and Google BigQuery can cost-effectively scale and process data within minutes with latency in minutes or seconds.
This allows data engineers to skip preload transformation and ingest raw data on the go.
This means you don't have to write complex transformations as a part of the data pipeline. You don't even have to deal with not-so-scalable on-premises hardware.
Data quality is the foundation of data integration.
It will affect the analytics, and the final derivations concluded from the data. Latency is the time needed for a single unit of data to travel through the pipeline.
Maintaining low latency can be expensive in terms of processing resources and cost. It is highly recommended to strike the right balance to ensure that you can extract the most value from analytics.
Metadata and schemas are often overlooked in a data ingestion process, but if paid attention, this information can help you streamline dataflow.
Metadata often includes crucial information like the shape of a data structure, the table name, the number of bytes in a table, the field length, the data definition language, the table indexes, the relationship between different entities, and so on.
Keeping a record of metadata helps you discover, retrieve, use, and preserve data.
When dealing with big data ingestion, employing the best data ingestion tools and connections is highly recommended. While you can ingest data manually, using the tool speeds up the process.
Here are some of the best tools to assist you with the data ingestion process:
Portable is a popular data ingestion tool with 300+ ETL connectors and lightning fast custom development.
Portable supports unlimited data with hands-on support and maintenance.
Best for: Suitable for businesses seeking the long tail ETL connectors that you won't find on Fivetran.
Pricing: Comes in three versions: free, $200/month, and custom pricing for tailored business solutions.
Apache NiFi, based on the NiagaraFiles software by NSA (US National Security Agency), automates the flow of big data between different software systems.
The tool offers low latency, high throughput, and guaranteed delivery.
Best for: Managing data flows
Pricing: Open-source, free
Apache Kafka is an Apache-licensed open-source big data ingestion software known for its high throughput and low latency.
The tool helps you build high-performance data pipelines and assists you with effortless data integration.
Best for: Handling huge data pipelines
Price: Open-source, free
Apache Hadoop is an open-source framework that lets you store and process enormous datasets effortlessly.
Hadoop enables you to move and process massive datasets in parallel more quickly by allowing the clustering of multiple computers.
Best for: Processing gigabytes to petabytes of data
Price: Open-source, free
Dropbase lets you load offline data into live databases in real time. It allows users to perform various data cleansing and processing operations on data from Excel, CSVs, and JSON files and load it into a Postgres database.
The tool lets you perform quick data ingestion, loading, and transformation tasks, as needed.
Best for: Transforming offline data
Pricing: Usage-based pricing with a 14-day free trial
Amazon Kinesis is a cloud-hosted data service that fetches data streams and enables quick real-time processing and analysis.
The tool can capture terabytes of data per hour from thousands of data sources and load them into AWS data sources.
Best for: Processing real-time data
Pricing: $0.08 per GB of data ingested
Wavefront is a cloud-hosted streaming analytics service for data ingestion. The tool offers low latency and can achieve high data ingestion rates hitting millions of data points per second.
You can collect data from more than 200 services and sources and view it in custom dashboards.
Best for: Visualizing data
Pricing: $1.50/datapoint/second (PPS)
Matillion is an ETL tool for data ingestion into cloud-based warehouses. The tool helps you create a no-code, wizard-based data pipeline.
It provides pre-built connectors for popular data sources, including Google AdWords, Salesforce, Google Sheets, and more.
Best for: Perfect for organizations that need quick analytics for data coming from multiple sources
Pricing: Offers four plans: Free, Basic ($2.00/credit), Advanced ($2.50/credit), and Enterprise ($2.70/credit)
Stitch Data is a cloud-based ETL platform facilitating easy extraction, transformation, and loading of data. The tool allows you to fetch data from more than 100 SaaS sources.
The tool can be connected to eight data warehouses and data lake destinations. Users can also contact the Stitch team to build new sources.
Best for: Replicating data with no coding
Pricing: $100 - $1,250 per month, depending on scale, with a 14-day trial
Automated data ingestion introduces self-service in your workflow, making data extracted from different data sources available to the data analysts for better analysis.
Automated data ingestion also simplifies the process for non-technical employees. All they need to do is use an ETL tool to add and remove data sources and provide a destination for data replication. They can then derive important business insights quickly.
Portable is a data ingestion tool that helps you move tons of data from one point to another through its 300+ data sources. The tool also provides custom connectors along with hands-on support and maintenance.