Organizations that have implemented data lakes have been performing increasingly well, making the most of the data that is available. Enhanced revenue, augmented customer satisfaction, better productivity, increased business decision-making, and in-depth analytics are some of the many advantages observed clearly.
A data lake serves as a centralized and unified repository for various data-driven projects, storing data in its native format. It forms the fundamental component of the data architecture of many organizations and data is available as and when needed. It is used lavishly for big data analytics, predictive modeling, machine learning, and data science applications.
The global data lake market size was valued at USD 7.6 billion in 2019 and is expected to grow at a compound annual growth rate (CAGR) of 20.6% from 2020 to 2027.
This article serves as a detailed guide to data lakes, their features and benefits, architecture, challenges, data lake tools, and frameworks, implementation process, etc. Before we read through further details, let us first glance through an overview of data lakes.
What is a Data Lake?
A data lake is a system or repository of data stored in its natural/raw format, usually, object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data, etc., and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine learning. – Wikipedia
A data lake is a centralized repository that facilitates storing data in its raw format – be it structured, semi-structured, or unstructured. Users can store the data as is, and then execute different analytics, dashboard generation, or visualizations for getting real-time analytics and better decision-making.
Data lakes are competent to encompass hundreds of terabytes/petabytes of unedited data after storage of replicated information from heterogenous sources like text documents, images, web content, relational databases, SaaS platforms, CSV/XML/JSON files, emails, PDFs, audio, video.
They are implemented in cloud-based storage with tech stalwarts like Google, Microsoft, Amazon, and Oracle and offer a single place to access enterprise-level information. Businesses deploy data lakes in their traditional on-premises data centers or modern-day cloud-based architecture. There is more inclination for data lakes to be deployed in the cloud now with services like lake integration, automation, and management.
There is total support of leading cloud technologies for cloud-based object storage with services like Google Cloud Storage, Amazon S3, and Azure Blob Storage. Analytics, reporting, big data processing, on-premises data movement, cloud, and IoT data movement are some of the basic data lake use cases that are popular today.
There are various data lake technology vendors that offer robust solutions, some of the big ones being AWS Lake Formation, AWS Glue, Cloudera Data Platform, Databricks, Dremio, Google Cloud Data Fusion, Google Cloud Storage, HPE GreenLake, Azure HD Insight, Azure Blob Storage, Azure Data Lake Storage Gen2, Oracle, Qubole, Snowflake.
Salient Features of Data Lake
A data lake showcases certain key characteristics that make it easily accessible to all businesses alike, here are they:
- Limitless data repository
- Separate computing and storage
- Direct availability of source data
- Mixed data types
- Accessible to all data, be it any source or type
- Storage of data in raw format
- Diverse interfaces and APIs
- Modern access control process
- Optimal search, metadata, tagging
- Centralized and fully available data
Key Benefits of a Data Lake
Thanks to the above features, a data lake exhibits the following salient advantages to the industry segments, worldwide:
- Data science and analytics
- Identify business trends and patterns
- Risk management, fraud detection, maintenance
- Fewer IT resources and data management costs
- Elimination of duplicates in data platforms
- Installable on low-cost hardware
- Predictive modeling, machine learning, text mining
- Detailed data insights and data exploration
- Faster and more flexible than traditional ETL tools
- Accessible and affordable to all in the enterprise
- Comprehensive and compatible with data analytics methods
- Simple data pipelines and higher operational efficacy
- Enhanced client interaction
- Decreased data silos
Key Data Lake Concepts
There are certain data lake concepts that must be perceived for the data lake architecture:
- Data Ingestion – Empowering connectors to collect data from varied sources to load in the data lake
- Data Storage – cost-effective storage and faster availability for exploring data in different formats
- Data Governance – Access to data with its usage, security, and data integrity
- Security – Implementation of security features like authorization, and authentication while storing and consuming data to offer access to authorized users only
- Data Discovery – Understand the data before analytics with thorough organization and interpretation
- Data Quality – Ensuring high-quality data for effective business output, without which there could be degraded quality inputs
- Data Auditing – Evaluating risk and compliance to standards by tracing each change with respect to its data elements
- Data Survey – Finding out the apt dataset prior to kicking off data analytics
Challenges Associated with Data Lakes
Though the concept of data lakes looks quite simple, there are certain inbound challenges that are associated with it, that must be overcome for better execution, here are they:
- Conversion of a data lake into a data swamp that is completely unorganized and users may not be able to find the needed information from it
- Excess use of technology may lead to confusion and complication
- Lack of schema or metadata may make the data difficult to use
- Unavailability of integrated view across the organization
How to Implement Data Lakes?
While implementing data lakes, here are certain best practices that can help enterprises in extracting the best output:
- Find out the skill level and expertise that is needed to perform data analytics
- Carve organizational objectives and evaluation criteria prior to data lake design
- Study data sources and prioritize data based on requirements
- Implement a complete governance policy and regulation stands for security and integrity
- Find out all the data that must be analyzed for further use
- Establish uses cases for data and data scientists for gaining optimal business value
On a Final Note
In the world of data engineering, the data lake plays a pivotal role. Though it looks like a data warehouse, there are certain basic differences that will stay. Data lakes may be a data source for a warehouse through the ELT process. Data lakes may be used in different use cases as and when dealing with a huge amount of data required, irrespective of its structure, size, and format.
Data lakes are a boon to the world of business data and with the help of an experienced IT partner, businesses can indeed extract the best value from the hoards of data accessible to them. Contact us in case you have any data lake requirements, and we will be happy to hel
Note: This Post Was First Published On https://ridgeant.com/blogs/data-lakes/
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