What is Data Ingestion? How to Pick the Right Data Ingestion Tool?
This article is a comprehensive write-up on data ingestion. It will answer all your queries such as What is data ingestion? What are the present challenges organizations are facing ingesting the data in real-time, batches? How to pick the right data ingestion tool? I also talk about the underlying architecture involved in setting up the big data flow in our systems. Making sense of such a massive amount of data. What are the popular data ingestion tools available in the market?
So, without any further ado. Let’s get on with it.
1. What is Data Ingestion?
Data Ingestion is the process of streaming-in massive amounts of data in our system, from several different external sources, for running analytics & other operations required by the business. Data is ingested to understand & make sense of such massive amount of data to grow the business.
1.1 Where Does this Massive Amount of Data Come From?
The data is primarily user-generated, generated from IoT devices, social networks, user events are recorded continually which helps the systems evolve resulting in better user experience.
There is no limit to the rate of data creation. With passing time, the rate grows exponentially. As more users use our app, or IoT device or the product which our business offers, the data keeps growing.
Data ingestion is just one part of a much bigger data processing system. More commonly known as handling the Big Data. The data moves through a data pipeline across several different stages.
Also, there are several different layers involved in the entire big data processing setup such as the data collection layer, data query layer, data processing, data visualization, data storage & the data security layer.
The entire process is also known as streaming data in Big Data.
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1.2 What are the Different Ways of Ingesting Data?
Data ingestion can be done either in real-time or in batches at regular intervals. It entirely depends on the requirement of our business.
Data Ingestion in real-time is typically preferred in systems reading medical data like a heartbeat, blood pressure IoT sensors where time is of critical importance. In systems handling financial data like stock market events. These are a few instances where time, lives & money are closely linked.
On the contrary in systems which read trends over time. For instance, estimating the popularity of the sport over a period of time, we can surely ingest data in batches.
1.3 Why is Data Ingestion Important?
Businesses today are relying on data. They need user data to make future plans & projections. They need to understand the user needs, his behaviours. All these things enable companies create better products, make smarter decisions, run ad campaigns, give user recommendations, gain a better insight into the market. In short, creating value from data.
Which eventually results in more customer-centric products & increased customer loyalty.
There are also other uses of data ingestion such as tracking the service efficiency, getting everything is okay signal from the IoT devices used by millions of customers.
Centralizing records of data streaming in from several different sources like for scanning logs. Reducing the complexity of tracking the system as a whole. Scanning logs at one place with tools like Kibana cuts down the hassle by notches. I’ll talk about the data ingestion tools up ahead in the article.
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2. What are Some of the Real-World Industry & Architectural Use Cases of Data Ingestion?
Here are some of the use-cases where data ingestion is required.
Moving Massive Amount of Big Data into Hadoop
This is the primary & the most obvious use case. As discussed above, Big Data from all the IoT devices, social apps & everywhere, is streamed through data pipelines, moves into the most popular distributed data processing framework Hadoop for analysis & stuff.
Moving Data from Databases to Elastic Search Server
In the past, with a few of my friends, I wrote a product search software as a service solution from scratch with Java, Spring Boot, Elastic Search. Speaking of its design the massive amount of product data from legacy storage solutions of the organization was streamed, indexed & stored to Elastic Search Server. The streaming process is more technically called the Rivering of data.
As in, drawing an analogy from how the water flows through a river, here the data moved through a data pipeline from legacy systems & got ingested into the elastic search server enabled by a plugin specifically written to execute the task.
Log Processing, Running Log Analytics Systems
If your project isn’t a hobby project, chances are it’s running on a cluster. Monolithic systems are a thing of the past. With so many microservices running concurrently. There is a massive number of logs which is generated over a period of time. And logs are the only way to move back in time, track errors & study the behaviour of the system.
Now, when we have to study the behaviour of the system as a whole comprehensively, we have to stream all the logs to a central place. Ingest logs to a central server to run analytics on it with the help of solutions like ELK stack etc.
Stream Processing Engines for Real-Time Events
Quick real-time streaming & data processing is key in systems handling LIVE information such as sports. It’s imperative that the architectural setup in place is efficient enough to ingest data, analyse it. Figure out behaviour in real time & quickly push information to the fans. After all, the whole business depends on it.
Let’s talk about some of the challenges the development teams have to face while ingesting data.
3. What are the Challenges Companies Face When Ingesting Data?
Guys, data ingestion is a slow process. How? I’ll explain. When data is streamed from several different sources into the system, data coming from each & every different source has a different format, different syntax, attached metadata. The data as a whole is heterogeneous. It has to be transformed into a common format like JSON or something to be understood by the analytics system.
The conversion of data is a tedious process. It takes a lot of computing resources & time. Flowing data has to be staged at several stages in the pipeline, processed & then moved ahead. Also, at each & every stage data has to be authenticated & verified to meet the organization’s security standards. With the traditional data cleansing processes, it takes weeks if not months to get useful information on hand. Traditional data ingestion systems like ETL ain’t that effective anymore.
Complex & Expensive
As already stated the entire data flow process is resource-intensive. A lot of heavy lifting has to be done to prepare the data before being ingested into the system. Also, it isn’t a side process, an entire dedicated team is required to pull off something like that.
There are always scenarios were the tools & frameworks available in the market fail to serve your custom needs & you are left with no option than to write a custom solution from the ground up.
The semantics of the data coming from externals sources changes sometimes which then requires a change in the backend data processing code too. The external IOT devices are evolving at a quick speed.
So, these are the factors we have to keep in mind when setting up a data processing & analytics system.
Data is Vulnerable
When data is moved around it opens up the possibility of a breach. Moving data is vulnerable. It goes through several different staging areas & the development team has to put in additional resources to ensure their system meets the security standards at all times.
4. Data Ingestion Architecture
Data ingestion is the initial & the toughest part of the entire data processing architecture.
The key parameters which are to be considered when designing a data ingestion solution are:
Data Velocity, size & format: Data streams in through several different sources into the system at different speeds & size. Data streams from social networks, IoT devices, machines & what not. And every stream of data streaming in has different semantics. A stream might be structured, unstructured or semi-structured.
The frequency of data streaming: Data can be streamed in continually in real-time or at regular batches. We would need weather data to stream in continually. On the other hand, to study trends social media data can be streamed in at regular intervals.
5. How to Choose the Right Data Ingestion Tool?
I’ve listed down a few things, a checklist, which I would keep in mind when researching on picking up a data ingestion tool.
1. The data pipeline should be fast & should have an effective data cleansing system. It should be easy to understand, manage. Should be easily customizable to needs.
Could obviously take care of transforming data from multiple formats to a common format. Also, the data transformation process should be not much expensive. Going through the product features would give an insight into the functionality of the tool.
2. An upside of using an open-source tool is you can use it on-prem. Customize it, write plugins as per your needs.
3. The tool should comply with all the data security standards.
4. It should not have too much of the developer dependency. A person with not so much of a hands-on coding experience should be able to manage the stuff around.
For instance, it always helps to have a browser-based operations UI with which business people can easily interact, run operations as opposed to having a console-based interaction which would require specific commands to be input to the system.
5. The tool should have the feature of providing insight on data in real-time. Look into the architectural design of the product. See if it integrates well into your existing system.
Be clear on your requirements. What kind of data you would be dealing with? What is your data management architecture? Can the tool run on a single machine as well as a cluster?
Can it scale well? Can it handle change in external data semantics? The data pipeline should be able to handle the business traffic. The network is unreliable. It should resilient to network outages.
6. After you zero in on the tool, see what the community has to say about that particular tool.
6. What are Some of The Popular Data Ingestion Tools?
Here is a list of some of the popular data ingestion tools available in the market.
Apache Nifi – Apache Nifi is a tool written in Java. It automates the flow of data between software systems.
Gobblin By LinkedIn – Gobblin is a data ingestion tool by LinkedIn. At one point in time, LinkedIn had 15 data ingestion pipelines running which created several data management challenges. To tackle that LinkedIn wrote Gobblin in-house.
Apache Flume – Apache Flume is designed to handle massive amounts of log data.
Apache Storm – Apache Storm is a distributed stream processing computation framework primarily written in Clojure. The project went open source after it was acquired by Twitter.
Elastic Logstash – Logstash is a data processing pipeline which ingests data from multiple sources simultaneously.
Recommended Read: Master System Design For Your Interviews Or Your Web Startup
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I am Shivang, the author of this writeup. You can read more about me here.
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