Successful Data Ingestion (What You Need to Know)

This blog introduces you to data ingestion, what it is and why you need it. We outline the benefits that data ingestion offers, potential challenges and the best practices to follow for successful data ingestion outcomes. More importantly, we present a checklist for the attributes a high-quality data ingestion tool must have, and why we think BryteFlow fits the bill.

What is Data Ingestion?

Data ingestion refers to the movement and aggregation of data from multiple sources into one target platform, typically Cloud-based where it can be queried, analyzed, or stored. The multiple sources could comprise of transactional databases, data lakes, IoT devices, ERP and CRM applications, SaaS applications, on-premise databases etc. Data from different sources is thus extracted, cleaned, converted into a uniform, consumable format and loaded to the target (data warehouse, data lake or data mart) where it can be accessed and used by the organization. Learn about BryteFlow Ingest

Why do you need Data Ingestion?

Data creation today is proceeding at breakneck speed. New sources, new media, new applications, new IoT devices are throwing up a lot of data in a hundred different data formats. A lot of that data needs to be collected, made sense of, and used for driving business decisions and insights. That’s where data ingestion can help. Data ingestion brings all this data together in a centralized data hub where it can be consumed by data analysts, data engineers, business users etc. for their specific use cases ranging from querying, analytics, reporting, or even training Machine Learning models. Ingesting data helps in gauging market trends, making sales projections, and understanding and profiling customers and their requirements. Any business that isn’t doing this is destined to be lunch for the competition. Postgres CDC (6 Easy Methods to Capture Data Changes)

Data Ingestion Types

Broadly data ingestion is of 3 types. The method you adopt will rely on your objectives, your business, how soon you need the data, your IT setup and budget. Replication with SQL Server CDC

Real-time Data Ingestion or Streaming

Real-time data ingestion also known as streaming ingestion, collects and sends data from different sources to the target in real-time. As soon as there are changes or new data in the source, the data ingestion layer recognizes it and immediately extracts, processes, and loads the data to the destination. Change Data Capture is one of the mechanisms for ingesting real-time data. Automated data ingestion tools like BryteFlow using log-based CDC, merge changes (updates, inserts, deletes) automatically on target. Real-time data ingestion syncs data without impacting the performance of source systems. A well-known, real-time streaming tool is Apache Kafka, an open-source, message broking platform that provides distributed event streaming for real-time data pipelines and applications. About Kafka CDC

Real-time Data Ingestion Use Cases

Streaming ingestion is a must in businesses where an immediate reaction to new information is needed. For e.g. requesting a ride via the Uber app, stock market trading, IoT sensor data from mining equipment, online shopping transactions, monitoring of electricity grids, airline ticket bookings etc. Real-time data ingestion also fuels business insights and decisions through real-time data pipelines. Oracle CDC (Change Data Capture): 13 Things to Know

Batch Data Ingestion or Batch Processing

Batch Processing collects and groups data from different sources incrementally and later transfers this data to the target in batches. The batches can be scheduled for taking place automatically or run when triggered by user queries or applications. Batch processing helps to enable analytics on large datasets and usually costs less to implement than real-time data ingestion, which needs to monitor source systems constantly.

Batch Processing Use Cases

Batch processing is used by organizations that do not need real-time data but may need data for generating regular reports daily or weekly. ETL pipelines can support batch processing too. Batch processing use cases include weekly or monthly billing, inventory processing, maintaining attendance sheets, payroll processing, supply chain fulfillment, subscription cycles etc. The Easy Way to CDC from Multi-Tenant Databases

Lambda Data Ingestion

Lambda Data ingestion is a hybrid combination of real-time data ingestion and batch processing. The data is collected in groups using batch processing and real-time data ingestion. The Lambda architecture comprises of 3 layers – batch layer, serving layer and speed layer. The batch and serving layers index data in batches while the speed layer indexes data in real-time – data that has not been ingested by the first two layers. This approach provides a complete view of the historical batch data, while simultaneously providing low latency and avoiding data inconsistency.

Lambda Data Ingestion Use Cases

Lambda-based data ingestion helps in analyzing logs after ingesting them from multiple sources, in anomaly alerting and analyzing patterns. Lambda also enables Clickstream analysis so user interaction data can be processed in real-time, to gain insights into user behavior, providing website recommendations or optimizing websites. Lambda ingestion can help with social media analysis by processing social media feeds, customer feedback and analyzing trends.

Data Ingestion Benefits

Data ingestion is the cornerstone of your data integration efforts. Here are some benefits that accrue when data ingestion is done the right way. Learn about BryteFlow Ingest

Data ingestion makes data available to users

Data ingestion collects data across different organizational sources and converts it, so it is easily accessible to users for analysis and consuming applications. It processes the data from different sources and aggregates it into a unified dataset so that it can be analyzed or consumed by BI tools.

Data Ingestion helps in enhancing business insights

Trends, future predictions, and planning for growth need data collected over time as well as real-time data ingestion. Automated data streaming, change data capture data and historical data provide fodder for analytics and BI tools, that can provide powerful business insights and direction to modern businesses. Real-time Replication with SQL Server CDC

Data Ingestion transforms data to a consumable format

Data ingestion tools used in ETL pipelines transform multi-format data from applications, databases, IoT devices, data lakes etc. into a consumable format (e.g. Parquet or ORC) and provide a predefined structure before loading it to the destination.

Data ingestion can improve user experience for applications

Data ingestion can be used to ensure fast movement of data through applications and tools and to deliver a better user experience to users.

Data ingestion automates a lot of manual tasks leading to cost efficiency

Data ingestion tools can automate tedious manual data tasks, freeing up your engineers and data scientists to focus on their priorities rather than wasting time on unproductive tasks. This also leads to higher ROI and a reduction of data costs. As an aside, BryteFlow Ingest our data ingestion tool automates data extraction, change data capture, data merges, masking, schema and table creation, and SCD Type2 history, while providing data conversions out of-the-box, no coding required. How BryteFlow Works

Data Ingestion Challenges

The challenges in effective data ingestion are many, here we discuss some of them.

Data ingestion with manual coding can be a lengthy process

If you are using a manual process to ingest data, you might have found firsthand how cumbersome and full of delays it can be. Adding more sources, dealing with growing data volumes, can be problematic and increase latency. Manual mapping, extracting, cleaning, and loading can also have their own potential issues. In such cases automated data ingestion tools like BryteFlow Ingest can help a lot.

ETL pipelines for Data Ingestion are getting more complex

Data types, variety of sources, and data velocity are growing every day. As data grows exponentially, performance challenges in data ingestion may crop up. Data quality could get compromised. Data ingestion frameworks need to have scalability and flexibility built in to handle future requirements.

Data Ingestion process should not compromise security

When ingesting data from multiple sources to destination, data may need to be staged a few times. The more stops your data must negotiate, the higher the chances of a security breach. This is even more so in the case of sensitive data. BryteFlow can load your data directly to repositories like Snowflake on AWS or Azure using Snowflake’s internal staging which increases security.

Schema changes at source may not be reflected properly while ingesting data

Changes at source in the schema or data structure may catch data engineers off-guard and can have an adverse effect on the data ingestion pipeline. The ingestion may halt, or new tables may be created by automated ingestion tools on target, affecting data transformation and other events in the pipeline.

Data ingestion can result in missing, incomplete or duplicate data

Data ingestion pipelines can sometimes fail for whatever reason. This can result in data being lost, incomplete records and stale data. On the flip side, you may have duplicate data that arises from the re-running of jobs due to system or human error. A good data ingestion tool like BryteFlow performs seamless data reconciliation as a parallel process, alerting you in case of missing or incomplete data.

Data ingestion costs can add up

Adding new data sources, growing data volumes leading to a need for additional storage and servers, the need to maintain and monitor ongoing data ingestion implementations with an expert data engineering team, not to mention troubleshooting glitches, can add up to hefty data ingestion costs. How to reduce Snowflake costs by 30%

Best Practices for Data Ingestion

Knowing all the ways your data ingestion implementation could go wrong, is there a way to minimize the risk of mishaps? Yes, with data ingestion best practices. Here are some simple data ingestion best practices you should follow.

Data Ingestion Best Practice 1: Use an Automated Data Ingestion Tool

A no-code, high-quality data ingestion tool like BryteFlow can alleviate a lot of concerns. For one, you will not be held ransom by manual coding for data ingestion which could take months to deliver data, even more so with addition of new sources and formats. Data ingestion tools automate recurring, repeated tasks with log-based or event-based triggers, they do not need much involvement of DBAs, and adding new sources can be as simple as a couple of clicks. Data ingestion tools often have some quality control built in and reduce human error. They can speed up delivery of data remarkably, leading to faster business insights.

Data Ingestion Best Practice 2: Set up Alerts and Monitoring

If you have alerts set up at source for potential issues, it can save you a lot of time later and reduce impact on downstream processes. Alerts will notify you about errors when they occur – for e.g. in case of missing, invalid or incorrect data, issues in data transmission, data security breaches etc. Alerts should be instituted at various junctures in the data ingestion process so errors can be fixed as soon as possible. Before loading the data to target, it should also be checked for null columns, invalid data, and duplicate records etc. to ensure data quality.

Data Ingestion Best Practice 3: Keep a Copy of Raw Data

Before applying data transformation, ensure you have a copy of the raw data in read-only form that nobody should have update access to. In case the data needs to be processed again, this will help you use your original data without the hassle of re-obtaining it. The copy of raw data serves as a backup in case something goes wrong and as a point of reference to check the accuracy and data completeness of the transformed data. BryteFlow Blend for Transformation

Data Ingestion Best Practice 4: Have Realistic Expectations and Timelines

The data management team, business leaders and project managers may have different ideas about how much time the data ingestion implementation should take. Set up realistic timelines and expectations for data delivery considering the number and type of sources, type of data ingestion and testing required. Have clear communication in place so everybody is on the same page.

Data Ingestion Best Practice 5: Data Ingestion Pipeline should have Idempotentcy

An idempotent data pipeline is one which no matter how many times it runs and loads data from a source into a relational database, the result remains unchanged. This allows the data ingestion process to be repeated without generation of duplicate data. This makes for a self-correcting data pipeline and prevents duplicate records. Delete, Insert, Upsert and Merge operations help in achieving idempotence. Interestingly BryteFlow automatically merges deltas with data to provide updated accurate data.

Data Ingestion Best Practice 6: Document Your Data Pipeline

It is always helpful to preserve a record of your data ingestion pipeline. Not only will this help you understand the data pipeline better, but also serve as a useful reference to others and newer members of the team. It may help in troubleshooting should you face issues with the data ingestion in the future, or even help foster a better understanding in case of overly complex data pipelines. It will also help in handing off when there are changes in staff. Documentation should be easy to understand, maintain and re-use. It should include:

The objective of the data pipelines and their place in the data workflow
Notes about data sources and pipeline output
Steps and tools used at each step of the pipeline
Limitations and assumptions while creating the pipelines
Code snippets, configuration files that are relevant to the data ingestion

Data Ingestion Best Practice 7: Ensure the Ingestion Process is Scalable

Build the data ingestion process for scalability, in case there is a surge in volumes, the process should carry on without a hitch. This can be done by using parallel processing and distributed systems.

Data Ingestion Best Practice 8: Manage the Metadata

Get the metadata relevant to the data ingestion such as source, timestamp, and data lineage. Have a metadata catalog in place to easily track and query ingested data.

Data Ingestion Best Practice 9: Ensuring Security, Compliance and Governance

Make sure that the data ingestion process is in line with data security, compliance, and governance policies of your organization. Security measures could include access control, masking and encryption of data, data quality checks, and monitoring and auditing data activities with logs, dashboards and reports. You must also define data governance policies to state how the data will be collected, stored, shared and used. Data collection should also be compliant with industry-specific standards like HIPAA (healthcare), GLB (financial services) etc.

Types of Data Ingestion Tools

No-Code and Low Code Data Ingestion Tools

Data ingestion processes are of various types, ranging all the way from hand coding to no-code data ingestion tools. By far the most efficient method of data ingestion is by using automated data ingestion software. Data ingestion tools include open-source tools as well as commercial tools. Some tools are native and meant for ingesting data to specific Cloud platforms like AWS DMS for AWS Cloud, AutoLoader for Databricks, Snowpipe for Snowflake, Azure Data Factory for Microsoft Azure, pgloader for Postgres Database etc. Some are third-party tools like Matillion, Fivetran, Qlik Replicate and our very own BryteFlow. Usually third-party tools feature a point-and-click interface with a high level of automation, and built-in transformation functionality so hand coding is not needed. SQL Server to Snowflake: Introducing an Alternative to Matillion and Fivetran

Data Ingestion with Hand Coding

If you have expert data engineers on your payroll you can think about hand coding your data ingestion implementation. Hand coding can provide a high degree of customization but can prove to be a lengthy task, prone to delays. A timeline of weeks can stretch to months if specifications are changed, new sources are added, schema is changed etc. Also, hand coding may not be suitable for ingestion of very large datasets. Hand coding may be done using languages such as Python, Java, Node.js, REST, Go and .NET SDK.

What a Good Data Ingestion Tool Looks Like (A Checklist)

The Data Ingestion Tool must be scalable to handle large data volumes

Data volumes are growing like never before. Your organization needs a data ingestion tool that should be immensely scalable to handle huge volumes of enterprise data. The Easy Way to CDC from Multi-Tenant Databases