Big data helps businesses generate new growth opportunities, outperform existing competitors, and provide a seamless customer experience. However, to get the best out of data and thrive in this digital world, enterprises should possess well-curated, good quality data lakes that will empower digital transformation across an enterprise.
Table of Contents:
- What is a Data Lake?
- Advantages of Developing a Data Lake
- How to Build a Robust Data Lake Architecture
- Data Lake Architecture vs. Traditional Databases and Warehouses
- Data Lake Architecture Best Practices
- Help Your Data Thrive with Integrate.io
What is a Data Lake?
A data lake is a central location that handles a massive volume of data in its native, raw format and organizes large volumes of highly diverse data. Whether data is structured, unstructured, or semi-structured, it is loaded and stored as-is. Compared to a hierarchical data warehouse that saves data in files or folders, a data lake uses a flat architecture to store it.
The term data lake is often linked with Hadoop-oriented object storage. Organizations load data into the Hadoop platform, then they apply business analytics and data mining tools to the information where it resides on Hadoop's cluster nodes.
Nevertheless, it is important to note that Hadoop technologies do not represent an architecture even though they're used in building lakes. A data lake should have a good strategy and architecture set in place.
Advantages of Developing a Data Lake
There are several benefits of acquiring your own data lake, including:
- Ability to collect all types of structured and unstructured data in a data lake
- More flexibility
- Ability to store raw data—you can refine it as your understanding and insight improves
- Unlimited ways to query the data
- Application of a variety of tools to gain insight into what the data means
- Ability to derive value from all types of data
- Elimination of data silos
- Democratized access to information via a unique, centralized view of data across the organization
How to Build a Robust Data Lake Architecture
Key Attributes of a Data Lake
A data lake should present three key characteristics:
- A single shared repository of data: Hadoop data lakes keep data in its raw form and capture modifications to data and contextual semantics throughout the data life cycle. This approach is especially beneficial for compliance and auditing activities.
- Includes orchestration and job scheduling capabilities: Workload execution is a prerequisite for enterprise Hadoop. YARN enables resource management and a central platform to perform consistent operations, security, and data governance services in Hadoop clusters, assuring analytic workflows have access to the data and the computing power they need.
- Has a collection of workflows to execute: Easy user access is a data lake's hallmarks since organizations preserve the data in its original form. Data owners can then merge customer, supplier, and operations data, eliminating technical—and even political—roadblocks to sharing data.
Data Lake Architecture: Key Components
- Security: It is crucial to think of this aspect, especially during the initial phase and architecture. It’s not like relational databases, with an artillery of security mechanisms.
- Governance: Monitoring and supervising operations will become vital for measuring performance and improving the data lake.
- Metadata: Data that provides information about other data, so mostly all the schemas, reload intervals, etc.
- Stewardship: Depending on the organization, the role can be assigned either to a separate team or transfer this responsibility to the owners (users).
- Monitoring and ELT processes: as data is going from the Raw Layer, you need a tool to organize the flow through the Cleansed to the Sandbox and Application layer as you will often need to apply transformations
Data Lake Architecture vs. Traditional Databases and Warehouses
Data lakes, data warehouses, and traditional databases have different analysis paradigms:
- Data Lake
- Ingest Data
- Define Data Structure
- Relational Data Structuring
- Ingest Data
- Report Data Structuring
- Ingest Data
Data lakes allow organizations to save a lot of work and time that is usually invested in creating the data structure, thus enabling fast ingestion and data storage. Implementing the data structure as the last step is beneficial when the structure itself is hard to define and subject to changes or different interpretations.
Recommended Reading: Data lake vs Data Warehouse
Data Lake Architecture Best Practices
Digital transformation demands knowing authentic and accurate data sources in an organization to reliably capitalize on growing volumes of data and generate new insights that propel growth while maintaining a single version of the truth.
A robust and effective data lake should accomplish these criteria:
- The ability to work on all data types, with massive volume and high velocity.
- Reduced effort to ingest data.
- Facilitate advanced analytics scenarios.
- Store large volumes of data cost-efficiently.
These are some of the best practices to build robust data lakes:
1) Identify and Define the Organization's Data Goal
The first essential step in avoiding data swaps is clarifying what data the organization needs to collect and its business objective. Organizations with a clear data strategy reap benefits in avoiding data silos, incorporating an information-driven culture to maintain customer-centricity, scale-up, and meet the modern-day data environment's demands. Gathering massive volumes of data should not be the sole aim of organizations.
2) Implement Modern Data Architecture
The old data architecture models are not enough and may not satisfy the requirements of today's data-driven businesses cost-effectively. These following workflows help ensure modern data architecture:
- Data profiling: Organizations encounter an immense challenge in maintaining and benefiting from the massive volume of unstructured data. Data profiling enables organizations to build an understanding of current data assets and their condition. Thus, better-managed data and a maximum value in the future.
- Data cataloging: Often, data collected in one business section may provide value to other business sections in different situations. Nevertheless, business users may not always have the needed clarity about which data exists and its corresponding ownership. As a result, information is rarely used beyond its context, and plenty of opportunities to extract maximum value from data are lost. A data catalog will help manage and sustain the current datasets and KPI glossary.
- Data backup and archives: Data backup protects an organization's data on the cloud, whereas data archive solutions are for continuous data possession at an insignificant cost for more extended periods.
3) Develop Data Governance, Privacy, and Security
Data governance and metadata management are significant in maintaining a robust and effective data lake strategy. A well-curated data lake contains data that's clean, easily accessible, trusted, and secure. As a result, this high-quality data can be easily consumed with confidence by the business users. It is of absolute importance to build responsibility for data.
4) Leverage Automation and AI
Due to the diversity and speed of data coming into the data lake, it is crucial to automate the data acquisition and transformation processes. Organizations can leverage next-generation data integration, data storage, and analytical techniques to classify, analyze, and learn from the data quickly and with better accuracy.
5) Integrate DevOps
DevOps processes help to build and maintain a reliable data lake. Organizations need to establish clear guidelines on where and how data is collected, ensure those standards are always respected, evaluate sources as "trustworthy," and take preventive measures to guarantee it remains that way.
Recommended Reading: Building a Successful Data lake Architecture
To generate insights that support your business objective, you should be able to manipulate your data and process it through multiple data operations and transformations, such as aggregations. However, these operations depend heavily on the available architecture. In data warehouses, ELT processes are beneficial to perform transformations using a query language coupled with the database's processing capacity. The increasing number of operations relying on the database can massively impact the project's cost and complexity. Thus, many organizations adopt data lakes to decrease friction and complexity in their IT infrastructure and operations by using ETL tools to provide powerful engines for in-memory operations and support the different data transformations without setting a database structure.
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