With the evolution of connected digital ecosystems and ubiquitous computing, everything one touches produces large amounts of data, in disparate formats, and at a massive scale. Companies must harness this vast oil well of information, aka "big data,” to harvest insights. However, to succeed, one needs to capture or ingest large data files from countless and dynamic sources into a data management system to be stored, analyzed, and accessed.

Data ingestion is the opening act in the data lifecycle and is just part of the overall data processing system. Data ingestion occurs when data moves from one or more sources to a destination where it can be stored and further analyzed. The data may be presented in different formats and come from various sources, including streaming data, weblogs, social media platforms, RDBMS, application logs, etc. Without it, one has no data to maintain, cleanse, archive, or analyze. In short, data ingestion is the act of taking in data and making it accessible for general business use.

Table of Contents

  1. Batch vs. Stream Processing
  2. Data Ingestion Challenges
  3. Tools Of The Trade
  4. Popular Data Ingestion Tools
  5. Data Ingestion and Integrate.io

Batch vs. Stream Processing

It is possible to carry out data ingestion in two different phases: batch and stream processing (real-time). Batch processing applies to a block of data that is already in storage for some time. For example, a financial firm may batch process all the transactions performed in a 12-24 hour window. This type of data would usually contain millions of records; hence the job will take time. It is not an instant process and is not compatible with real-time scenarios where quick analytic results are required. It is best for producing more granular results, be it at a slow pace.

Stream processing allows one to process data in real-time and quickly detect conditions within a short period from receiving the data. This style of processing analyzes the data before it hits the disk. 

There are a few systems where Stream processing is critical:

  • Fraud detection: One can detect anomalies that signal fraud in real-time, which stop fraudulent transactions before damage occurs. 
  • Medical: Systems reading medical data like a heartbeat, blood pressure IoT sensors, where time is critical. 
  • Financial: High-frequency trading would not be possible without stream processing.

There are multiple open-source stream processing platforms such as Apache Kafka, Apache Flink, Apache Storm, Apache Samza, etc.

Batch processing works with all data, and stream processing deals with data over a rolling window or most recent record. Batch handles large swathes of data while stream handles individual records or micro-batches. Batch processing latency will be in the minute to hours timeframe while stream processing latency will be in seconds or milliseconds.

It is worth mentioning the Lambda architecture, which is an approach that mixes both batch and stream (real-time) data processing. It makes the combined data available for downstream analysis or viewing via a serving layer, divided into three layers: the batch layer, serving layer, and speed layer.

Check out the whitepaper here.

Data Ingestion Challenges

Many factors can severely impact data ingestion processes, causing a domino effect on the entire pipeline's performance. Of course, challenges are not just technical. They are all-encompassing, with financial impact as well as becoming a drain on resources and precious human capital. Let's discuss a few possible impediments on the journey.

1) Cost

Most assume that the data cleansing process only occurs for downstream analysis. This approach results in significant bottlenecks and can compromise compliance and data security regulations, compounding an already overly complicated and costly process. Also, verification of data access and usage can be problematic and time-consuming. Of course, this work needs an entire team of specialists. The infrastructure required to support the various data sources and patented tools can also be very costly to maintain in the long run. 

2) Legal 

As mentioned earlier, legal and compliance requirements add complexity (and expense) to the construction of data ingestion pipelines. For example, European companies have to comply with (GDPR) General Data Protection Regulation. US healthcare data is affected by the Health Insurance Portability and Accountability Act (HIPAA). Companies using third-party IT services require auditing procedures like Service Organization Control 2 (SOC 2). 

3) Volume and Variance

With the rapid increase in IoT devices, data sources volume and variance continue to explode exponentially. Hence, extracting data using traditional data ingestion approaches becomes a challenge, not to mention that existing pipelines tend to break with scale.

4) Velocity

Consider the speed at which data flows from various sources such as machines, networks, human interaction, media sites, social media. This movement can either be massive or continuous and poses a significant challenge to existing data ingestion processes and the data pipelines supporting them. Data continues to grow in complexity, and time-consuming bottlenecks inherently hit data ingestion pipelines as a result. It is impacting real-time processing scenarios the most. 

Tools Of The Trade

The following is a checklist to keep in mind when researching a data ingestion tool. There is no one size fits all approach, and the best system is a composition of multiple instruments; this list will help you build the best hybrid.

1) Extraction

Extraction is one of the most fundamental requirements of a data ingestion framework. Modern cloud data warehouses have the processing capability to manage write operations on large data sets efficiently. Cloud data warehouses are incredibly fast at processing data. They have essentially rendered ETL unnecessary for many use-cases - this has ultimately given rise to a new data integration strategy, ELT, which skips the ETL staging area for speedier data ingestion and greater agility. ELT sends raw, unprepared data directly to the warehouse and relies on the data warehouse to carry out the transformations post-loading.

Related Reading: What is ETLT?

2) Utility

The pipeline should be fast, customizable, and intuitive. There should be a built-in data cleansing system that helps to obey geographic compliance and data security regulations.

3) Scalability

The tool should be able to scale and remain operational under high load and within unreliable networks. Can the tool run on a cluster?

4) Usability 

The tool should not require in-depth technical knowledge to operate, must have sufficient abstractions for low cognitive load and knowledge sharing.

5) Integration 

Should integrate into your current system without too much disruption.

6) Security 

The best data ingestion tools utilize data encryption mechanisms and security protocols to secure the company’s data: i.e., HTTPS, SSH, and SSL.

7) Community 

Is there a healthy, constructive, and active community surrounding the tool?

The following is a list of some popular data ingestion tools available in the market.

1) Apache Nifi

Automates data movement between disparate data sources and systems, resulting in fast and secure data ingestion fast. The features that stand out are guaranteed delivery, visual command & control, and Flow Specific QoS (latency v throughput, loss tolerance, etc.) 

2) Gobblin

Gobblin is a data ingestion tool by LinkedIn for extracting, transforming, and loading large volumes of data from various data sources, e.g., databases, rest APIs, FTP/SFTP servers, filers, etc., onto Hadoop. Gobblin provides out-of-the-box adaptors for all of the commonly accessed data sources such as S3, Kafka, Google Analytics, MySQL, and Salesforce. However, it is highly extensible, and one can add custom adaptors at will and share them with other developers in the community (plug n play).

3) Apache Flume

Flume is essentially a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.  It guarantees reliable message delivery as it uses channel-based transactions. However, it is not 100% real-time, and one should be aware of this. Consider using Kafka if this is a strict requirement.

4) Apache Storm

Apache Storm is a distributed stream processing computation framework primarily written in Clojure. Storm remains highly performant under increasing load by adding resources linearly - provides guaranteed data processing even in the event of node failure in the cluster or lost message scenarios.

5) Elastic Logstash

Logstash is a data processing pipeline that ingests data from multiple sources simultaneously. Logstash usually resides within the ELK stack; ELK is an acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Logstash has recently become very popular for handling sensor data in Industrial Internet of Things (IIoT) use cases. One of the main reasons for this is the complete variety of data Input accepted (i.e., files, HTTP, IMAP, JDBC, Kafka, Syslog, TCP, and UDP).

Data Ingestion and Integrate.io

As mentioned before, one of the most fundamental requirements of a data ingestion framework is the ability to extract and process data. Integrate.io is a powerful, enterprise-grade ETL as a service platform that makes it easy for anyone – regardless of their tech experience – to create and automate sophisticated data integration processes. 

With Integrate.io’s powerful data engine, you can follow the ETL or ELT model as required. You can also adhere to the ETLT model by performing simple data preparations in-pipeline and directing the data warehouse to perform more nuanced SQL-based transformations after loading. 

Whether it’s pre-load or post-load transformations – or using ETLT for a mix of both – Integrate.io makes data integration a snap. If you’d like to try Integrate.io for yourself, schedule a demo with us.