Are You Thinking of Investing in Big Data Ingestion? What Should You be Looking at?
For companies that generate high volumes of data and growingly complicated data pipelines, Big Data has a huge number of potential uses, but it’s not right for everyone — so if you’re thinking of investing in Big Data Ingestion, here’s some things to consider.
What is Big Data Ingestion?
Firstly, let’s tackle what Big Data is — basically, as the name suggests, we’re talking about large, fast, structured data (often defined with the three ‘V’ — Volume, Velocity, Variety).
Data Ingestion is the first stop in the Big Data architecture, and it’s responsible for gathering together the data from various sources (such as databases, data warehouses, data lake houses, Internet of Things [IoT] devices, etc).
This first stage is an absolutely crucial point in the process and affects every decision made with the data, and how the data pipelines are structured going forward.
How is Big Data Ingestion predicted to grow in 2024?
According to LinkedIn, 2.5 quintillion bytes of data are created each day, and NewVantage Partners discovered that since 2019 investments in Big Data cost savings and regulations have quadrupled — basically there’s a huge amount of data and companies are spending more on making sure its usable.
All data has a life cycle (check out this blog on Ardent for more information on that), and it’s important that it’s made accessible and usable as soon as possible in order to maximise the business benefits.
With more focus on Artificial Intelligence (AI), business automation, sophisticated machine learning and increased data analysis, it’s unsurprising to find that Big Data is expected to continue growing in 2024 and beyond.
Being able to ingest the data and handle the information effectively is already an important task, and this won’t change in the coming year.
What popular tools are used to handle Big Data Ingestion?
Choosing the right tools to manage your data is a business-critical task, and it’s important that you get it right — some of the most popular tools on the market for handling Big Data Ingestion include:
- Apache Flume
- Sqoop
- GitHub
- Amazon Kinesis
- Wavefront
- Apache NiFi
- Coefficient
- Precisely Connect
Of course, there is also the option to develop your own software, this process is tricky and can be extremely expensive if you’re not prepared or have the right software provider (Ardent again providing some excellent advice here) — so it needs to be considered carefully.
What should you ask yourself before spending your budget?
Big Data structures aren’t suitable for every business, and before you commit to a budget and plan, at a minimum you should be asking:
- How integral is data collection, analysis, and usage to your business?
- Do you need to use your data in real-time?
- What is your current data architecture? Can it support new processes, or does it need overhauling?
- What value are you expecting from your data?
- How large are the datasets you intend to work with?
Having a clear idea of the size, scale, and structure of your planned data ecosystem is essential, but if you’ve got a lot of data, and are in an industry where analytics and information can help you get ahead — then big data may well be a worthwhile investment for you.
