Processing raw data involves several challenges and investments. Firstly, a significant investment in the right skills and experience is crucial. Professionals with expertise in data processing techniques and tools are needed to effectively handle and manipulate the data.
Additionally, a deep understanding of the best use cases for each data storage technology is essential. Different data storage solutions, such as databases, data lakes, or data warehouses, offer distinct advantages and limitations. Therefore, it is important to determine the most suitable technology for the specific data processing requirements. Investments in infrastructure and technology are also necessary. Reliable and scalable hardware and software infrastructure must be in place to process large volumes of raw data efficiently. This may include powerful servers, storage systems, and data processing tools or frameworks.
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.
A data lake is a centralized and highly scalable repository that is designed to store raw and unprocessed data from various sources. Unlike traditional data storage systems, a data lake allows users to store data in its original format without requiring any predefined structure or schema. The purpose of a data lake is to enable organizations to perform different types of analysis on this raw data, resulting in valuable insights that can drive improved decision-making.
To build a data lake, a series of manual steps are involved, making the process complex and time-consuming. Initially, data from diverse sources needs to be loaded into the data lake, ensuring the data flows are monitored effectively. The data may also need to be partitioned for efficient querying and analysis purposes. Additionally, security measures such as encryption need to be set up and appropriate key management strategies implemented. Another important aspect of building a data lake is the deduplication of redundant data to optimize storage and improve data quality.
However, simply creating a data lake without considering the right technology, architecture, data quality, and data governance can result in what is known as a “data swamp.” This refers to a situation where the data lake becomes an isolated pool of cumbersome, difficult-to-use, and hard-to-understand data that is inaccessible to users.
Data Lake architecture works by integrating various tools, services, and techniques to efficiently manage data ingestion, storage, retrieval, analysis, and transformation processes. In a Data Lake architecture, data is stored in a format that allows for the incorporation of diverse data types and sources to derive valuable business insights.
Key components of a typical Data Lake architecture include:
- Resource manager: Responsible for allocating appropriate resources for different tasks within the Data Lake.
- ELT (Extract, Load, Transform): This process involves extracting data from various sources, loading it into the Data Lake‘s raw zone, and then cleansing and transforming it for analysis.
- Connectors: Enable users to easily access and share data in preferred formats through various workflows.
- Data classification: Supports functions such as profiling, cataloging, and archiving, allowing teams to track changes in data content, quality, history, storage location, and more.
- Analytics service: This service should be fast, scalable, and distributed to support different data workloads in multiple languages.
- Data security: Ensures data protection through features such as masking, auditing, encryption, and access control, whether the data is at rest or in transit.
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
|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
|Designed prior to data warehouse implementation (schema-on-write)
|Written on the time of analysis (schema-on-read)
|Fastest query results in using a higher cost storage
|Query results getting faster using low-cost storage
|Curated data that serves as the primary source of information
|Any data - structured or unstructured
|Data scientists, big data engineers, and business analysts (when using structured data)
|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.
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 can your organization afford?
Considerations for architecting cost-optimized data storage involve understanding the differences between data warehouses and data lakes and evaluating the specific needs and capabilities of an organization.
Data warehouses serve as large-scale repositories for various types of structured data, making it easy to identify patterns and insights through data analysis. However, building and maintaining a data warehouse requires a significant investment, including specialized skills and experience.
By comparison, data lakes are suitable for organizations with data specialists who can handle data mining and analysis. They are particularly beneficial for automating pattern identification using advanced technologies like machine learning and artificial intelligence. Data lakes also function as scalable online archives, allowing organizations to store vast amounts of data that may not require immediate transformation or analysis.
When it comes to cost optimization, organizations should consider the resources required for each data storage technology. Data warehouses involve substantial investments in terms of skills, experience, and understanding of specific use cases. On the other hand, data lakes can provide cost savings by eliminating the need for immediate data transformation and analysis.
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.
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.
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.
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.
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.
Lake Formation offers a range of capabilities that effectively handle data preparation and cleaning tasks. One of its noteworthy features is the utilization of machine learning (ML) techniques to enhance data consistency and quality. Using ML algorithms, Lake Formation can efficiently clean and deduplicate data, thereby improving its overall quality.
Another essential capability provided by Lake Formation is the ability to reformat data, making it suitable for various analytics tools such as Apache Parquet and Optimized Row Columnar (ORC). This ensures that the data is correctly structured and optimized for efficient analysis and processing.
Also, Lake Formation incorporates an ML-powered transform known as FindMatches. This transformative tool enables users to match records present in different datasets, facilitating the identification and removal of duplicate records. The process is exceptionally streamlined, typically requiring minimal to no human intervention, resulting in improved data integrity and accuracy.
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:
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. Data lakes and data warehouses are not direct competitors but instead work together in a complementary manner to enhance data management and analytics capabilities. Utilizing a data lake as a central repository for raw and unstructured data, organizations can store large volumes of diverse data without a predefined structure.
This empowers data warehouses to efficiently query and analyze structured and processed data. The data lake acts as a staging area, allowing data engineers and data scientists to explore, clean, and transform the data before loading it into the data warehouse. By carefully selecting the best data lake solution along with a top data warehouse solution, organizations can maximize the value and usability of their data. AWS managed services for data lakes and data warehouses, combined with the expertise of APN Consulting Partners in designing and implementing these solutions, ensure that your business is equipped with the most suitable technologies for effective data management and analytics. This comprehensive approach enables organizations to make informed decisions and derive actionable insights from their data.
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