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5 Easy Steps For Your Data Lake Journey

Are you interested in making your data lake function well? Do you want to take your big data to the next level? Are you wanting to learn how to use unstructured data? Then you came to the right place. This is an easy 5-step guide that will help you start your journey using big data, unstructured big data, and leveraging everything a data lake has to offer. Don't wander in the dark with this easy 5-step process.

5 Easy Steps For Your Data Lake Journey

Why a Data Lake?

In the world of big data, unstructured data is a major player. It's often difficult to wrangle unstructured data into a format that can be used by traditional data systems. You can spend a lot of time trying to get your unstructured data into a system that will eventually spit it back out at you in an unusable format.

That's where data lakes come in handy. Data lakes are a way to store and retrieve your unstructured data so that it's useful. Data lakes allow you to store all of your information in one place, using the same system for both structured and unstructured data. 

You don't need to spend days changing the formats of your different datasets until they're usable, because you've got one central location for everything you need.

What Goes in Your Data Lake?

A data lake is a storage repository that holds a large amount of raw data in its native format until it's needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the user can filter the stored information based on the metadata tags and identifiers.

The key difference between a hierarchical data warehouse and a data lake is that the warehouse is designed to store structured data, while lakes are designed to store structured and unstructured data. Unstructured data cannot be organized into fields or columns, while structured information can be processed by relational databases that require all data to have the same organization, structure, format and length.

Data lakes are often used for storing unstructured big data such as social media posts, digital images and videos. Lakes are also used for storing IoT sensor streams, machine logs and other time-series-based information. By storing this unstructured information in its original format, businesses can use these lakes to perform ad hoc analytics on their business operations with relative ease.

Where do I Start?

If you're anything like me, you've heard about data lakes and unstructured data, and you've been excited to add them as a resource. But then you go to actually use them, and… it's not so easy.

You're not sure where to start, or how to make sense of the information in an unstructured data set. Maybe you're wondering if there's a best way to analyze big data. Or maybe you haven't even gotten that far. Maybe you're still just thinking about where the heck that data is going to come from!

I've been there, and I'm going to help you get where you need to be with this guide on how to use big data in your business. We'll tackle everything from how to find and acquire useful unstructured data sets, to how—and when—to use them as a part of your business strategy.

How Do I Use the Data Afterwards?

Data lakes are often referred to as “big data” and have little structure, but that doesn't mean that you can't use them effectively.

Unstructured data is really just a fancy way of talking about information that isn't stored in a pre-defined way. It has no structure, no format, and no rules. When you're dealing with unstructured data, you get all the information at once, and then you decide how to use it.

Maybe the best way to talk about what unstructured data is, is to talk about what it isn't: structured data. Take a credit card company, for example. They get a ton of information from their customers every day. Everything from purchase history to call logs to account balances to social media activity is collected on the company's servers at any given moment.

This information comes in through databases and other systems that are designed specifically for collecting that type of information. When the information gets collected, it gets organized by type. For example, all purchase history goes into one database and all social media activity goes into another database (and so on). These databases are pretty organized; they're set up so that it's easy to find the info you want when you need it. 

My Biggest Regret - What I would have done differently

As a company, we've made some mistakes. That's to be expected when you're starting out and trying to get your footing in an industry as complex as big data management. But what if we'd known then what we know now? What would we have done differently?

As it turns out, there are plenty of things we'd change in hindsight. For example, we didn't understand the difference between a data lake and a data warehouse early on, so there was some wasted time and energy on our part. It's something that many companies struggle with—how do you know when to use which tool for the job? If you're still searching for the answer, watch our video about how to choose the correct tool for your business' needs.

We also used a lot of unstructured data at first, not realizing that unstructured data is harder to work with. We spent a lot of time writing custom code to extract information from this kind of data and ended up losing half of it in the process. Sound familiar? Check out our blog post on using unstructured big data—you'll find ten tips for making it more accessible and easy to integrate into your current practices.

Using data lakes isn't as hard as you might think!

It's true: using data lakes isn't as hard as you might think. In fact, it's a whole lot easier than you'd expect for an unstructured big data-related platform.

We've really opened up the conversation about using data lakes, with our articles on big data and how to use unstructured data. But we've never spelled it out in one place, so here you go:

  • Data lakes work because they're essentially just like your average collection of data—except that they haven't been formatted yet. The biggest difference between a data lake and a regular database is that a regular database is managed by software that assigns every piece of information a specific format, and if that piece of information doesn't fit into the format, it can't go in the database. A data lake has no such software; instead, it's free-form.

  • So why use a free-form format? Data lakes are ideal for situations where you don't know exactly what kind of information you're going to need in advance—for example, when you know you want to do some analysis on consumer complaints from social media but you aren't sure what questions your analysis will answer or what kind of answers it will give you.

Step 1: Get Data

Let's get ready to party.

Collecting data is like going out for a night on the town with your BFFs. You want to make sure you have the right look, the right attitude, and know where you're going. When you get there, you're ready to take it all in. And when you leave, you have a story worth telling.

That's what using a data lake is all about. It helps you collect the information that's relevant to your business and use it to drive your next steps with confidence. Data lakes offer a simple, highly scalable solution for businesses looking to bring together their data into one place—whether that data is structured or unstructured. With tools like [company name], you can easily gather and store the information that matters most to your company, whether it comes from internal sources or external ones (like social media).

Once all of your company's data is in one place, it becomes much easier for everyone involved to do their jobs better. From product development to marketing, having all of your company's information in one location means that everyone can tap into the insights they need at any time—making them more productive than ever before!

Step 2: Organize and Index

  • Get the data you need into storage. Start by copying files into the lake.
  • Index and catalog the data. Do this in two ways: Metadata: Store detailed information about each file (e.g., author, date created, tags). A schema: Describe each file based on common features (e.g., word count, image type, number of rows and columns).
  • With indexing, you add labels to your data to describe what it contains.

Step 3: Prepare for Analysis

  1. Because most of the data in data lakes is not accessible by traditional analytics systems and tools, you need different tools and a different approach to analyze big data.
  2. To analyze big data in a data lake, you need a new way of approaching analysis. You can't use the same approaches with big data that you would with small amounts of structured data.
  3. The first step in analyzing big data is getting organized. The process of organizing your resources to do analysis is called metadata management, and it's essential for success when working with unstructured or semi-structured data.
  4. Metadata management involves defining what each block of information in your data lake refers to. For example, if you're analyzing health care claims, each claim includes several categories of information like the name, age, location and gender of the patient, their relationship to the person filing the claim, diagnosis codes and more. In order to determine how much more you know about one category (say, people living in a specific city) than another (people living in all other cities), you must first be able to separate out all of those fields from each other so that you can look at each category individually.

  5. In this phase you’ll get ready to perform analysis.
  6. Make sure you have the correct tools and skills to analyze the data lake.
  7. Use a good tool that gives you access to all the data lake components, so you can work with unstructured and structured data from the same location.
  8. Now that your data is in an accessible format, you can query it along with your structured data sources, for deeper insights.
  9. If you can access your data lake in a straightforward way, and do so alongside your usual structured data sources.

Step 4: Analyze

  1. Thanks to your data lake, you’ve moved in the right direction, with a lower cost of storage and greater flexibility in how you process unstructured data.
  2. Now you can make the move to structured analysis—with no data movement.
  3. This makes it possible to do analysis on larger datasets at lower cost and with greater flexibility, even when data is in a raw format.
  4. You can discover hidden trends and dive deep into your data, enabling new insights that weren’t possible before.
  5. Cloud analytics turns large volumes of raw data into actionable business insights, through advanced analytics like machine learning and artificial intelligence (AI).
  6. Analyze your unstructured big data with machine learning and artificial intelligence

Step 5: Operationalize

  1. It’s the moment of truth. You’ve spent months building a big data lake, and now you want to put it to work.
  2. Real-world analytics integrates data from multiple sources.
  3. The best algorithms are not always the most accurate.
  4. Security is critical.
  5. Always continue to improve.
  6. Make sure your company can scale with the data you are using.
  7. Properly implemented, data lakes enable organizations to boost productivity, foster innovation, and generate new revenue streams.

Key takeaways for using your data lake for unstructured big data

Big data has been a hot topic in the world of digital marketing for years. But what is it? Big data is simply large amounts of digital information, often unstructured, that can be analyzed to find patterns and trends.

Unstructured big data has been especially difficult to work with because it doesn't lend itself well to traditional database structures. In a data lake, however, all sorts of unstructured data—from documents and emails to images, video, and audio files—can be stored together. Data lakes are also maintained on cloud systems, which allows for quick analysis and easy access from anywhere at any time.

So how do you get started working with big data and your new data lake? Here are some key takeaways:

  • Develop a strategy. You can have all the data you want, but if you don't know what you're looking for or why you're looking for it, it won't do you much good. Before investing in a new system like a data lake, decide what sort of goals you want to achieve through your big data analysis and make sure they align with your overall business goals.

Hopefully, these five steps have given you a good starting point for your Data Lake journey. There are many other ways to utilize data lakes, and ways that companies can use them for more than just Big Data applications. By experimenting with all of the possibilities, and establishing what works best for your company, you will be able to make the most of your data lake investment, and improve your organization's customer experience.