Crunching big data in a data lake and/or data warehouse – Although the two have similarities, collecting and accessing data in a lake or warehouse environment differs in many ways. The types of data each accepts, and the ease of analysis are two major differences. Using either can result in better business intelligence but leveraging both best benefits a firm’s bottom line.

Data Lake Defined

The term data lake refers to a centralized repository allowing structured and unstructured data storage at any scale. In a lake environment, information flows from line of business applications and non-relational sources like Internet of Things (IoT) devices, mobile applications and social media. Storing data as-is saves time. Despite its mishmash, a data lake still lets the user run analytics and access the information for big data processes and machine learning.

Data Warehouse Defined

The term data warehouse refers to an optimized database designed for relational data analysis of information flowing from line of business applications and transactional systems. Fast SQL queries define the data schema and structure. Within the warehouse environment, the data gets cleaned, enriched and transmuted into report form. With analysis applied, the user accesses the final product in the form of operational reports.

Data: Lake vs. Warehouse

Rather than choosing between a lake or warehouse environment, most organizations use both. Each serves a separate need.

Choosing the lake lets the user pick from a diverse range of queries. Unlike the warehouse, which requires establishment of SQL queries at its outset, the lake lets the user apply queries on the whole or to pieces and explore newly developed information models. Gartner calls it the Data Management Solution for Analytics (DMSA).

The warehouse proves a better choice for presentation of polished data. It efficiently processes relational data from line of business applications, operational databases and transactional systems.

  • Data Lake
  • Data Warehouse
  • Structured Data
  • Unstructured Data
  • DMSA
  • Schema

Each choice has its positives and negatives. These include:

  1. Schema * a warehouse features schema-on-write, * a lake features schema-on-read,
  2. Performance vs. Price * a warehouse provides the fastest query results but has higher storage costs, * a lake provides slower query results but lower storage costs,
  3. Data Quality * a warehouse provides highly curated data with strong truthing, * a lake houses raw data and curated data,
  4. Users * a lake gets commonly used by business analysts, * a warehouse gets commonly used by business analysts, data developers and data scientists,
  5. Analytics * a warehouse lends itself best to batch reporting, business intelligence and visualizations, * a lake lends itself best to data discovery, data profiling, machine learning and predictive analytics.

How Data Lakes Work with Big Data Needs

These lakes of data work better with newer analytics types like machine learning. A study by Aberdeen revealed that organizations leveraging a lake for data analysis experienced nine percent greater organic revenue growth than their peers. That’s because the lake let the business leaders identify and act on business growth opportunities more quickly by:

  • drawing and keeping new customers,
  • increasing productivity,
  • making well-informed decisions,
  • maintaining devices.

Lakes and Analytics

Not all lake and analytics platforms are created equally. Consider the following key capabilities when choosing a solution:

  1. Data movement: You can import any amount of real-time raw data from multiple sources whether a small batch or big data in scale. It saves time by negating the need for a pre-existing schema and data structure.
  2. Cataloging and Storage Security: This choice lets you catalog, crawl and index relational and non-relational data, but requires security measures to protect data.
  3. Analytics: Many lake options let you use a variety of analytics tools on the data in place. There’s no need to move data to analyze it.
  4. Machine Learning: Leverage data in the lake for machine learning without moving it. It also lets you include historical data and build forecast models.

data lake

Using the Lake Methodology

Using the lake methodology can lead to better customer interactions and research and development innovations plus an increase in operational efficiencies. This option lets you combine data from a various sources, including a CRM platform, incident tickets, a marketing platform and social media analytics to identify reasons for customer churn and potential options to increase customer loyalty. With respect to improved research and design, it can help test a hypothesis, reduce assumptions and analyze results. Finally, this option lets your organization increase operational efficiencies by allowing you to automatically collect, analyze and store real-time information from Internet of Things (IoT) devices.

Lakes do present certain challenges though. It contains a morass of raw data. It requires developing suitable security measures. It also needs defined queries or mechanisms created in order to conduct analysis. Before applying security measures and queries the lake is essentially a swamp. Lakes tend to be most easily implemented in the cloud. The cloud environment provides availability, performance, reliability and scalability. Cloud implementation also offers a faster deployment time, instant functionality updates and enhanced geographic coverage.

How Warehouses Work with Data

A warehouse can only amass and provide an analysis field for multiple heterogeneous sources. Its queries must be designed at the outset. Once incorporated into the warehouse, it can’t be changed. You can run analytics on historical data.

It also differs from a standard database, a transactional system monitoring and updating real-time data to provide the most recent data only. The warehouse aggregates structured data historically.

Creating a Warehouse for Data

While lakes can accept any form of data and in raw format, a warehouse needs a specific structure. Follow these steps to create a warehouse for data.

  1. Extract the data from multiple source points.
  2. Compile the data.
  3. Clean the data thoroughly. Check and correct for errors.
  4. Convert from database format to warehouse format.
  5. Sort the data.
  6. Consolidate the data.
  7. Summarize the data.
  8. Add data as it becomes available.

This process produces well secured data that’s easily retrieved, reliable and manageable. Data stored in this manner can easily be mined. Business analysts use it to acquire insights to improve business processes. Warehouses make it simple for various departments to share data.

db code

Using the Warehouse Methodology

Implementing a warehouse for data represents a key component of a business intelligence program, says The Data Warehouse Institute. This centrally located, permanent home for business data allows access for all business intelligence functions from advanced analysis to reporting. Although they’re expensive, their key benefits justify the cost.

It provides enhanced business intelligence

The warehouse coalesces data from multiple departments and enables inter-organizational sharing. Executives and managers can base decisions on analysis from a cohesive and up-to-date data set. This contributes to the elimination of uncertainty in business forecasting and reduces risk by providing improved data analysis that “can be applied directly to business processes including marketing segmentation, inventory management, financial management, and sales,” states BI Insider.

It provides a time saving mechanism

Warehousing data saves time in two key ways. It organizes critical data from a variety of sources and departments into one central pool. This eradicates the data gathering step. The warehouse environment also provides a simple querying method that executives can use themselves. This eradicates the need to involve the information technology in the generation of reports. This means management can conduct on the fly research of data during brainstorming sessions to accurately examine idea feasibility without a significant time investment.

Warehousing enhances both consistency and quality of data

The cleaning, sorting and conversion of data in a warehouse implementation turns data from numerous sources and systems into a common format. This standardization ensures reporting viability across departments. This highly accurate data provides a better source for business intelligence decisions.

The warehouse method provides historical intelligence

A warehouse of data stores large amounts of historical data so you can analyze different time periods and trends in order to make future predictions. Such data typically cannot be stored in a transactional database or used to generate reports from a transactional system.

Using a warehouse of data generates a high return on investment

Examining the bottom line of firms that use the warehouse method combined with a complementary business intelligence system, they do provide a high return on investment (ROI). Those companies generated more revenue and saved more funds than firms with no warehoused data and business intelligence system.

Pick up the phone and call us to learn more about implementing a lake of data using Amazon Web Services (AWS) and warehousing data. Lakes provide the most comprehensive, cost-effective, scalable and secure service options. They let a firm build and analyze data in the AWS cloud. AWS already hosts customers like FINRA, iRobot, NASDAQ, Netflix and Zillow. Firms co-leverage a warehouse to enable a wider range of data analysis and ensure clean, optimized data that’s easy to analyze. Let us help you join those already leveraging the lake and the warehouse.