Particularly in terms of throughput, consistency, and resilience, modern applications frequently place high demands on their data analysis pipeline.
A data analysis pipeline is typically a collection of scripts that work together to process large amounts of data.
Some scripts could take days or many hours to finish, therefore it’s critical that they function in a responsive and predictable manner.
Yet scalability problems plague contemporary data analysis pipeline.
Typically, they work quickly and successfully at the beginning but struggle as more and more data is processed via them.
When this occurs, it typically signifies that either the developer(s) manually scaled the pipeline up or down, or that the product had a built-in scalability problem.
The typical outcome of this is that the data analyst either has to wait hours or days for the pipeline to finish processing everything or has to make the painful discovery that the pipeline is simply not scalable and needs to be completely redesigned.
Several contemporary programs, including Apache Spark, Hadoop, and Flink, weren’t built with the heavy lifting that the contemporary data analysis pipeline demand in mind, resulting in sub-optimal performance compared to what could be accomplished in a well-optimised data analysis pipeline.
This article will explore the various ways you can scale your data analysis pipeline and the benefits (and challenges) that come with each, whether you’re a seasoned data analyst looking for a change from manual tasks, a recent CS graduate looking for a challenging job, or perhaps you stumbled upon this blog article searching for career advice.
Three Main Challenges
Even if you start processing a lot of data with the greatest of intentions, you will run into three serious challenges:
The first challenge that you will encounter is throughput.
The amount of data that can be processed in a given amount of time is called throughput. Your throughput will be 30 GB/sec if, for instance, you need to process a 1 GB file every 30 seconds (1 GB / 0.3 seconds = 30 GB/sec).
Although this is a sizeable amount, it is considerably less than the 100 MB/sec (100 MB / 0.3 seconds = 100 MB/sec) that a conventional CPU can process.
In comparison to hardware-based solutions like a hard drive or a powerful GPU, the software will be less efficient the lower the throughput.
Latency will be your next obstacle to overcome. The time it takes to process data, including any necessary network traffic, is known as latency.
Most storage devices are slow, and neither are networks. The processing will take longer the more distance the data has to go.
Network latency will be a consideration, for instance, if you need to get data from a database.
In addition, a lot of contemporary databases weren’t built for quick queries.
For instance, high-speed queries were not considered when creating the popular SQLite database.
As a result, your database will have a relatively low maximum query rate if it is configured by default (max query rate is the maximum number of queries that can be performed per unit of time).
If the maximum query rate for your database is large, many requests will need to be queued up and executed sequentially, adding a lot of latency.
You can either utilise a database that was created expressly for high-speed queryings, such as the HSQLDB database or Apache Phoenix, or you can install and set up an embedded database in your application.
You’ll face memory difficulties as your third difficulty. Data storage capacity in a computer is referred to as memory.
Although storage devices (hard drives, SSDs, and the like) have far less memory than the central processing unit in most current computers, they do have a lot of memory (CPU).
As a result, if you have a sizable dataset, you will need to make a storage device (such as a hard disk, SSD, etc.) available to hold it all.
After the data is stored, it can be examined, leading to the creation of new data that also has to be kept.
The majority of data analysis pipeline have to deal with memory problems as a result of this never-ending cycle.
Although not a problem in and of itself, this may certainly be an unpleasant situation.
It’s possible, for instance, that you’ll run out of memory and be unable to carry out any productive computations.
You will need to either discover a means to process the data in stages or find a way to lessen the volume of data that you are now trying to handle.
Scaling Your Data Analysis Pipeline
You can scale your data analysis pipeline in a number of ways, which is fortunate.
Either manually scale up your hardware, which may require buying more powerful machines, or scale up your software by utilising a cluster of computers.
It’s simple to manually scale the hardware.
Simply make sure that the data analysis pipeline’s subsequent steps are just as quick as their predecessors.
You can employ more CPU cores, faster hardware, a combination of the two, or both to do this.
The software requires greater effort when manually scaling. Depending on your development environment, you have a variety of alternatives here.
Making use of a batch processor is the first choice.
Blocks of data are fed into a batch processor, which processes them in bulk.
Before calculating the findings and assigning a new block of data, the full batch must be completed.
If your data analysis pipeline is lengthy, this method can be quite useful because it will always ensure that your hardware is being used efficiently.
Choose a Data Analysis Pipeline Cluster-Based Approach
Using a computer cluster is another approach to expand your data analysis process.
A cluster is often a collection of computers that are linked by a quick and dependable network.
Installing a high-availability (HA) cluster management is required to guarantee that your cluster is operational around-the-clock.
Kubernetes, a Google-developed open-source container-based cluster manager, is a well-liked option for high-availability clusters.
It greatly simplifies running a cluster because it was created primarily for large-scale clusters.
Because of this, it is well-liked by data scientists and engineers who frequently work with clusters.
Scalability was considered when creating Kubernetes.
The cluster’s nodes can all join and leave at any moment thanks to this feature.
As a result, the cluster will be effectively portable and resilient, and nodes will be able to be added or deleted as necessary.
A Kubernetes cluster has numerous benefits and is extremely available, but it is not without drawbacks. For instance, managing a cluster of any size might involve a significant administrative burden.
Think About A Data Analysis Pipeline Managed Service
An entity known as a managed service provider (MSP) gives clusters and the individuals who work with them on-demand access so they can spin up resources as needed.
A typical MSP offers a comprehensive range of services, such as installation and administration assistance, hardware purchase (such as CPUs, hard drives, network adapters, and similar items), staff training, and more.
After the cluster has been configured and is being utilised by your application, an MSP will frequently also offer continuous support and maintenance.
For those looking for a quick and simple way to scale their data analysis pipeline, an MSP offers a cost-effective solution.
Your developers may concentrate on building the program because they will handle all management-related administrative tasks.
Keep Data Analysis Pipeline Network Traffic Low
There will always be a requirement to exchange data via a network when moving massive datasets back and forth between your systems.
The volume of traffic that your application will produce once you start processing a lot of data will rise dramatically.
Your internet service provider will need to handle more traffic as a result, particularly if you transmit and receive huge volumes of data every day (ISP).
To prevent this, you might think about utilising a VPN, or virtual private network, which establishes a private and secure connection between two or more people who are using unreliable public networks, like the internet.
Even when you only purchase a single subscription, which is frequently the case, many VPNs are incredibly quick and reasonably priced.
1. Distributed Computing (Docker, Spark, Hadoop)
If you’ve ever tried to analyse large data sets on a personal computer, you’ll know how cumbersome it can be.
Even SSDs (Solid State Drives) aren’t large enough to hold all the data you need if it’s in the terabytes; it will always fill up your hard drive if not properly organised.
This is where distributed computing comes in.
Docker is one of the most popular tools amongst data engineers and scientists for automating the deployment of data analysis software and services on large numbers of personal computers (or servers) across the globe – with all the attendant benefits of scaling out your infrastructure and reducing your manual workloads.
In a nutshell, Docker containers allow you to package up all the software and tools you need into a discreet unit that can be transported across computer networks and stored on servers (hence the name ‘distributed computing’).
Once the Docker container is on the server, it can be accessed via a familiar computing interface such as a web browser – just like you would if you were working on a personal computer.
Whether you’re running a business or academia, you can benefit from the fact that all your servers are now on the same platform and can be managed via a single interface.
It’s a clear advantage when you need to scale up your infrastructure in response to increased demand or if one of your servers breaks down (as long as you’ve got backups, of course!).
2. Data Analysis Pipeline Using the Cloud (Amazon Web Services, Google Cloud, Microsoft Azure, and others)
When you stop to think about it, all of our most crucial data is stored in the cloud.
Our pictures, documents, and films are kept here. Our phone, email, social media, and contact information are all stored there.
It is indisputable that storing your data on the cloud as opposed to on-premises is more affordable.
You gain the extra freedom of being able to view your data from any location.
And, if you’re utilising the right vendors, you can be sure that your data will be protected – ensuring that your business can continue to function even if a disaster were to strike.
3.Data Analysis Pipeline Modernising Legacy Systems (HANA, Snowflake, and others)
For businesses operating on a smaller scale, legacy systems such as SAP, Oracle, and Microsoft Systems can be a roadblock to data analysis.
Simply put, these are the systems your company has invested in and continues to use because they work – but they work slowly and with a high operating cost.
If you’re in the middle of a transition to the cloud, you’ll most likely face challenges relating to data migration – as all your systems (especially those running on-premises) will need to be upgraded to work with the new platform.
Thankfully, there are alternative options. One of the most popular solutions for business users is to install a HANA analytical database in the cloud and connect to it using familiar computer languages such as SQL – which can be used to access and analyse your company’s data.
HANA provides an easy-to-use interface for data analysts and business users to explore and analyse their data without needing special training.
Most importantly, HANA gives you the flexibility to deploy your database on multiple servers – allowing you to spread your workload and reduce the chances of downtime caused by server failures.
4. Virtualised Data Analysis Pipeline (Parallels and VMWare)
One computer may run various operating systems (such as Windows, Linux, and macOS) simultaneously thanks to desktop virtualisation software like Parallels and VMWare.
The primary benefit of virtualisation is the capacity to construct “virtual machines” that are equipped with all the required programs, configurations, and documentation to accurately reproduce the working environment of a real computer, including the operating system.
This implies that you won’t need to spend a lot of money on pricey equipment.
You can make as many clones of your data analysis environment using virtualisation software, enabling you to scale up quickly without requiring more physical gear.
Just be sure you have adequate storage to accommodate all of your virtual machines, as they will take up a lot of room when idle.
Make sure that you have adequate bandwidth to support all of your virtual machines because when they are active, they will consume a lot of your internet connection.
5. Algorithmic Data Analysis Pipeline (Stratagem, Tibbr, and others)
Most businesses operate on a large scale; they have thousands, if not millions, of customers that they need to serve.
As the saying goes, ‘data is something you want to get your hands on’, and most businesses have more data than they know what to do with. This is where algorithmic (or automated) data analysis pipeline comes in.
When you have large amounts of data, you can use algorithms to process the information and create valuable insights.
The main distinction between algorithmic data analysis and traditional data analysis is that with the former you don’t need to predefine the analysis – the software will analyse the data automatically and generate reports for you to review.
This can be a time-saver as well as allow you to focus on other aspects of your business.
6. Scaling Data Analysis Pipelines (HPC, GPU Computing)
If your data set is too large for your laptop or desktop computer, you can opt for high-performance computing (HPC) to scale up your data analysis.
Using a network of computers, HPC is a technique for analysing enormous amounts of data (usually supercomputers).
You can choose to outsize your infrastructure and invest in GPUs (Graphics Processor Units) to speed up the process of data analysis if you have sufficient cash and a manageable budget.
Even the biggest businesses find it difficult to handle their data on a single computer, much less a little one.
All of these pieces of gear and software need maintenance and upkeep, as you might think, and you’ll still have to pay for them even if you don’t use them.
But, it’s a rare circumstance for a business owner or manager that spending money on more processing power is unnecessary.
This post won’t go through every version of a data analysis pipeline; instead, it will provide you with a fast rundown of the most well-liked techniques for streamlining your data analysis procedure and assisting with rapid and effective scaling.
Understanding the different approaches to grow your data analysis pipeline will enable you to select the one that best meets your needs and makes the most use of your time.