Are you seeking a more extensive data storage solution for your business?
Information is the indispensable asset used to make the decisions that are critical to your organization’s future. This is why choosing the right model requires a thorough examination of the core characteristics inherent in data storage systems.
There are two main types of repositories available, each with diverse use cases depending on the business scenario. Although the primary purpose of each is to store information, their unique functionalities should be the guide to your choice, or maybe you want to use both!
What is the difference?In short, data warehouses are intended for the examination of structured, filtered data, while data lakes store raw, unfiltered data of diverse structures and sets.
In this article, we take a deep dive into the lakes and delve into the warehouses for storing information. After understanding what they are, we will compare/contrast and tell you where to get started. Consult the table of contents to find a section of particular interest.
Table of Contents
What is a data lake?
A data lake contains big data from various sources in an untreated, natural format, typically object blobs or files. This centralized repository enables diverse data sets to store flexible structures of information for future use in large volumes.
Simply store your data as-is, without prior assembly, and run different types of analytics. These can come from dashboards and visualizations to big data, real-time figures, and machine learning - all to guide better and more certain decisions!
What is a data warehouse?
A data warehouse is a centralized repository of integrated data that, when examined, can serve for well-informed, vital decisions. Data flows from transactional systems, relational databases, and other sources where they’re cleansed and verified before entering the data warehouse.
Data analysts can then access this information through business intelligence tools, SQL clients, and other diagnostic applications. Many business departments rely on reports, dashboards, and analytics tools to make day to day decisions throughout the organization.
Extract, transform, load (ETL) and extract, load, transform (E-LT) are the two primary approaches used to build a data warehouse.
Data lake vs data warehouse: key differentiators
| Characteristics | Data Warehouse | Data Lake |
|---|---|---|
| Content | Relational from transactional, operational databases, and line of business applications | Non-relational and relational from IoT devices, web sites, mobile apps, social media, and corporate applications |
| Schema | Designed prior to data warehouse implementation (schema-on-write) | Written on the time of analysis (schema-on-read) |
| Performance/Price | Fastest query results in using a higher cost storage | Query results getting faster using low-cost storage |
| Quality | Curated data that serves as the primary source of information | Any data - structured or unstructured |
| Users | Business analysts | Data scientists, big data engineers, and business analysts (when using structured data) |
| Analytics | Batch reporting, BI and visualizations | Machine Learning, predictive analytics, data discovery, and profiling |
Data lake vs data warehouse: why do I need them?
Businesses that leverage data to make informed decisions invariably outperform their competition.
Why?
Because their business decisions are rational, based upon accurate statistics. If you’re excelling in a particular area, then you should clearly concentrate on that sector. You can’t decide where to dedicate your resources when you are unable to locate the corresponding data!
Smartly processed information will help you identify and act on areas where there is opportunity. When applied by diligent experts such as AllCode, it attracts and retains customers, boosts productivity, and leads to data-based decisions.
A survey performed by Aberdeen shows that businesses with data lake integrations outperformed industry-similar companies by 9% in organic revenue growth.
Data lake vs data warehouse: coordinated
Often, organizations will require both options, depending on their needs and use cases; with Amazon Redshift, this synchronization is easily achievable.
The contents of a data warehouse must be stored in a tabular format in order for the SQL to query the data. However, not all applications require that data be in a tabular form. Applications like big data analytics, full-text search, and machine learning can access data that is partially structured or entirely unstructured with data lakes.
As the volume and variety of your data expands, you might explore using both repositories. Follow one or more common patterns for managing your data across your database, data lake, and data warehouse. See a few options below:
Data lake vs data warehouse: which is best for me?
Before you choose which option favors your business, consider the following questions and then look at some of the industries we have described and to see which line up with yours.
What type of data are you working with?
If you’re working with raw, unstructured data continuously generated in significant volumes, you should probably opt for a data lake. Keep in mind, however, that data lakes can well surpass the practical needs of companies that don’t capture significant, vast data sets.
If you’re deriving data from a CRM or HR system that contains traditional, tabular information, a data warehouse is the way to go.
What are you doing with your data?
Data lakes provide extraordinary flexibility for putting your data to use. They also allow you to store instantly and worry about structuring later. If you don’t need the data right away, but want to track and record the information, data lakes will do the trick.
If you’re only going to be generating a few predefined reports, a data warehouse will likely get it done faster.
What tools exist in your organization?
Maintaining a data lake isn’t the same as working with a traditional database. It requires engineers who are knowledgeable and practiced in big data. If you have somebody within your organization equipped with the skillset, take the data lake plunge.
However, if big data engineers aren’t included in your company’s framework or budget, you’re better off with a data warehouse.
Healthcare Industry
The healthcare industry requires real-time insights in order to attend to patients with prompt precision. Hospitals are awash in unstructured data (notes, clinical data, etc.) that require timely submission. Data lakes can quickly gather this information and record it so that it is readily accessible.
Education Systems
Big data in education has been in high demand recently. Information about grades, attendance, and other aspects are raw and unstructured, flourishing in a data lake.
Financial Services
In financial institutions, information is generally structured and immediately documented. This data needs to be accessed company-wide; therefore indicating a data warehouse for easier access.
Transportation Field
In the transportation industry, specifically supply chain management, you must be able to make informed decisions in a matter of minutes. Using data lakes, you get access to quick and flexible data at a low cost.
Data lake on AWS
AWS has an extensive portfolio of product offerings for its data lake and warehouse solutions, including Kinesis, Kinesis Firehose, Snowball, Streams, and Direct Connect which enable users transfer large quantities of data into S3 directly. Amazon S3 is at the core of the solution, providing object storage for structured and unstructured data - the storage service of choice to build a data lake.
With Amazon S3, you can efficiently scale your data repositories in a secure environment. Leverage S3 and use native AWS services to run big data analytics, artificial intelligence (AI), machine learning (ML), high-performance computing (HPC) and media data processing applications to capture an inside look at your unstructured data sets.
Start your data lake formation by visiting here:
http://aws.amazon.com/blogs/big-data/getting-started-with-aws-lake-formation/
Data warehouse on AWS
AWS is also a hub for all of your data warehousing needs. Amazon Redshift provides harmonious deployment of a data warehouse in just minutes and integrates seamlessly with your existing business intelligence tools.
To get started with data warehousing on AWS, visit here: http://aws.amazon.com/getting-started/hands-on/deploy-data-warehouse/
Data lake vs data warehouse partner
Transforming data into a valuable asset of utility to your organization is a complex skill which requires an array of tools, technologies, and environments. AWS provides a broad and deep arrangement of managed services for data lakes and data warehouses.
APN Consulting Partners have comprehensive experience in designing, implementing and managing data and analytics applications on AWS. They will determine the best solution for your business and ensure that you’re getting the most out of your data.
AllCode is an AWS Select Consulting partner that knows how to make data work better with analytics platforms, NoSQL/NewSQL databases, data integration, business intelligence, and data security.
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