Can A Data Lake And A Data Warehouse Work Together?
/The global movement to the cloud has been a central concern of businesses around the world - the new infrastructure providing an invaluable opportunity for data analysis. As the market continues to evolve, companies need to make advancements in data management strategies if they want to expand their digital capacities and stay on track with their competitors.
Considering that the IT spend to migrate to the cloud is costing businesses around US$1.3 trillion in 2022, enterprises across all industries are already making the leap towards enhancing their data infrastructure.
One of the central methods of doing so has been the turn to big data, using advanced external data in order to improve performance, boost scalability, and gather deep insight into the market they’re working in.
When turning to data solutions, Data Lakes and Data Warehouses are two common methods of accessing the necessary infrastructure. Yet, traditional data warehouses are costly, create data homogenization, and can be time-consuming to manage.
Even with tools at hand, according to the Forrester report, around 60% to 70% of all data within enterprises goes unused for analytics. By combining data warehousing with data lakes, which are much more scalable and hybrid data ecosystems, businesses can put themselves back on track to effective data use.
Instead of migrating from data warehouses to data lakes manually, cloud data warehouses have become a popular solution. These warehouses provide a hand-in-hand approach, using both data lakes and data warehouses simultaneously to provide a complementary fix to the problem.
In this article, we’ll discuss how these two data management systems coexist in the cloud and the benefits of each tool. Let’s get right into it.
What Is A Data Warehouse?
Data warehousing is a process of gathering data from various sources simultaneously. The data undergoes transformations using the Extract Transform Load (ETL) process, so it is structured for business analysis and reporting.
What Is A Data Lake?
A ‘Data Lake’ is an evolutionary step of data processing and storage. It's a centralized repository that holds raw data - both structured and unstructured, that can then be accessed at any time.
These data lakes keep data in its original form, ensuring data engineers can collate and analyze it when needed.
Key Differences Between Data Warehousing And Data Lakes
When comparing data warehousing and data lakes, several differences rise to the surface, with each of these infrastructures serving slightly different purposes.
We’ll be discussing:
● Data Types
● Processing power
● Agility
● Storage and Retention
● Security and Usage
Let’s take a look at the key factors that differentiate the two.
Data Types
- Data Warehouse - Stores structured enterprise data, including ERP, CRM, and financial transactions. It does not store web server logs, social media, and other data types.
- Data Lake - Stores all data types, even the ones not supported by the data warehouse.
Processing
- Data Warehouse - Organizes data in a structured form. It follows the ‘schema on write’ process.
- Data Lake - Keeps the data in raw form and follows the ‘schema on read’ process.
Agility
- Data Warehouse - Stores historical data with predefined structures. It is insufficient for in-depth analysis.
- Data Lake - Offers immediate access to any type of analysis.
Retention & Storage
- Data Warehouse - Removes unused data timely, which makes data retention impossible.
- Data Lake - Ensures a less complex process to data retention as it keeps both structured and unstructured data.
Security and Usage
- Data Warehouse - Offers a secure and mature enterprise technology used by large organizations.
- Data Lake - Is evolving and is used by data scientists, data engineers, and business analysts.
Cloud Data Warehousing Combines With Data Lake Solutions
Cloud data warehouse offers powerful computing capabilities; thus, delivering real-time analytics, faster data processing, and storage solutions. Moreover, it builds a one-window system for enterprises to leverage both data lake benefits and data management perks.
However, choosing the right cloud solution critically depends on your analytic business needs and general data. Some of the leading cloud data warehouses are listed below:
To make things easier, we’ll be discussing the top two platforms with in-depth analysis to compare their capabilities and specifications. So, let’s begin the comprehensive comparison of Redshift vs Bigquery.
With either of these tools, you’ll be able to integrate into a data lake, using cloud data warehousing services to synergize your business data and draw a more comprehensive insight from the industry.
What are these services?
Both acting as data warehouses, these two services are offered by Amazon and Google, respectively.
RedShift, Amazon's cloud data warehouse - Amazon RedShift is a fully managed, petabyte-scale cloud data warehouse solution. It enables you to gather new insights about your business and its clientele.
BigQuery, Google's cloud data warehouse - BigQuery is a serverless architecture offering a fully-managed enterprise data warehouse to analyze and manage your data. Google defines BigQuery as a highly scalable and cost-effective data warehouse.
RedShift vs. BigQuery Comparison
Let’s take a look at the difference between these services, looking at which will help your business integrate data lakes and warehousing through their cloud solutions. We’ll touch on their pricing schemes and the performance of these two tools.
Pricing Model
RedShift offers Dense Computer and Large Dense Storage capabilities. The lowest cost on RedShift is around $306 per TB per month, which covers both storage and processing services.
Moreover, it allows up-front payments that unlock major discounts. For instance, you can identify your business needs during the day and spin up each node. It will cut the overall cost dramatically.
On the other hand, BigQuery has a more complicated pricing model. It may look cheap, but as you go all in, you'll find the total cost exceeding the expected budget. The storage cost is around $20 per TB per month, charging separately for both storage and queries. The queries’ cost is about $5/TB, which, when added into the overall cost, makes it quite a lot larger than it first appears.
BigQuery offers the best solution to data scientists for carrying out the data mining process as you only have to run Query for spiky workloads.
Performance Evaluation
The price model of BigQuery is based on the amount of data you process, while RedShift is limited by the nodes you run. Additionally, the number of concurrent queries and the size of your data table also influence the performance of these distinct services.
There have been many benchmarks set to evaluate the performance, but none can be referred to as the standard. Hence, to evaluate the performance, you need to talk about the manageability both the solutions offer.
Manageability can be broken down into four aspects:
Security
As RedShift uses Amazon IAM and BigQuery uses Google Cloud IAM, both systems ensure security and efficiency.
Yet, Google also offers B2B identity management with OAuth that enables users to identify controls to 3rd parties while keeping their ecosystem secure.
Data types
When it comes to data types, BigQuery works with standard SQL data types along with sub-standard SQL; whereas, RedShift only supports standard SQL data types. With the use of Dremel capabilities, BigQuery supports nested data classes. However, you need to flatten out your data with RedShift.
RedShift and BigQuery are append-only; they both handle deletes and updates effectively. However, with BigQuery, the process is more expensive, with fewer options for user customization. With RedShift, Postgre Vacuuming is used to reclaim tables. The system, on the whole, offers efficient delete and update support.
Functionality
Hands down, BigQuery is the simplest to use. It handles the complexities of database configuration and offers hassle-free cluster management.
On the contrary, with Integrate.io, RedShift offers an easy to perform workflow to its users, which is definitely a benefit of this platform.
Integrations
Both systems offer a wealth of integrations. They support almost every data and BI analysis tool.
Apart from performance and pricing, if you ponder on the architecture of both platforms, you’ll find a major dissimilarity.
Amazon is focused on platform-as-a-service (PaaS) or infrastructure-as-a-service (IaaS). Google’s approach is to go with software-as-a-service (SaaS).
Who Wins?
Based on the comparison, the two leading systems - BigQuery and RedShift both stand out for their incredible cloud data warehousing solutions. There are minor differences in their products and a whole range of major similarities.
To make a final decision, you need to evaluate your business needs and the budget. You can forecast pricing with RedShift with its on-demand and hourly model. Whereas, BigQuery's $5/TB query cost can offer the best budget considering your business scenarios.
Final Thoughts
Whether you go with RedShift or BigQuery, it is evident that a data lake and data warehouse can coexist and work together in the cloud. While these two methods of storing data are not interchangeable, thanks to their different functions, they are complementary. Instead of firmly planting yourself as a business that either uses warehouses or lakes, take a more flexible approach.
A business can use state-of-the-art data warehousing tools to integrate with data lakes, with the two systems working side-by-side in the IT space.
The tactic of combining these services, pulling on what’s good about data lakes and what’s effective about data warehouses, can provide organizations with advanced functionality and greater data management benefits.
