Start Using Data Lakes for your Big Data Projects
Tags: Big Data Analytics, AWS, Apache Spark, Big Data, Microsoft Azure

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Big data is the term coined to refer to a vast collection of data, complex or growing fast with time. It is impossible to process using conventional data analytics methods. Examples of big data collections are social media site information, stock exchanges, health records, etc.

Data and analytics have emerged as a primary source of value generation for businesses. Improving recommendation systems enables music and podcast streaming companies to provide more personalized experiences to their customers, thus increasing revenue. Investment firms rely on large quantities of data to power artificial intelligence and machine learning to better manage portfolio risks using real-time market data.

Types of Big Data

Structured Data

Structured data is organized data. It can be an employee database in a multinational company, health records of a hospital, etc. The size of this data is increasing exponentially with time, but it is organized and easily accessible using simple search engine algorithms.

Unstructured Data

Data collected from social media sites is unstructured. The database does not have a defined schema. Data can come from a variety of sources like social media photos and videos, audio and text, PDFs and emails, all of which are generated on a daily basis. Data lakes and data warehouses can store such data.

Semi-structured Data

Semi-structured data includes both structured and unstructured data.

Big Data Use Cases

One can use big data for predictive analysis. Analysis of big data can predict outcomes that are useful for inventory and stock management, assisting in decision-making for a business.

Product Analytics

Data from social media sites provides insights into customer requirements as well as problems. One can use big data analytics to understand how well a product is doing. It helps businesses understand how to improve or revamp their products and use digital marketing strategies to enhance consumer experience. It also helps them understand which product or service to invest in or spend more time and energy improving. It helps them stay ahead of competitors. Data analysis can reveal patterns in sales, e.g., offers by rival companies, and calculated decisions can be taken.

Cost Reduction

Big data combines both structured and unstructured data from multiple sources to produce important insights. Big data analytics tools can help filter unwanted data, saving millions of dollars for companies.

Big data has already started creating a huge difference in healthcare and education, making it possible to create personalized solutions. The banking sector uses big data for fraud detection and card misuse in real time. Big data analytics is improving the supply strategies and product quality for manufacturing companies. Big data helps predict incompetencies and uncertainties that can affect the organization. Processing big data includes data mining, storage, real-time analytics, and visualization.

What Are Data Lakes?

Data lakes are centralized repositories for data storage. Here, data can be stored as is. It does not require a specific format. The data can be processed for machine learning and analytics. Data science and artificial intelligence methods can be used to discover patterns in time-series data.

Why should you use data lakes for your Big data Projects?

Businesses need to be able to identify and avail opportunities to escalate business growth. Data analysts play a pivotal role in generating business insights from big data for companies. They apply analytics, machine learning and data science methods to identify meaningful information from the social media data and data IoT devices stored in a data lake and data warehouse. This helps businesses take calculated decisions.

Benefits of using Data Lakes

As more and more businesses understand the uses of data lakes, they are changing their data warehouses to include data lakes. This enables data analysts to generate more meaningful information from the data. Predictive analytics and inventory management are helping several businesses gauge the demand for their product and prepare efficiently. This reduces wastage and storage costs.

Simplified Data Management

Data lakes allow you to add real-time data as it comes in. Data can be structured or unstructured. Data of any size can be added without defining any schema. This data can be analyzed through cataloging and indexing. Data lakes help users gain a deeper understanding of business situations because they have more context than ever before, accelerating analytics experiments.

Improved Customer Interactions

Data lakes can use data from social media analytics in combination with CRM platforms. This helps businesses understand their customers: what product they need and what to do to encourage them to purchase their products. This lets them stay updated with how their competitors are performing in the market as well.

Analytics

You don’t need to migrate data from data lakes to a separate analytics system to analyze it.

Data scientists, data developers, and business analysts access data from data lakes directly using analytic tools such as Apache Hadoop and Apache Spark.

Artificial Intelligence and Machine Learning

Data lakes make it possible for businesses to understand and analyze customers’ data. This helps use machine learning algorithms on the data for processes like demand forecasting.

Improved R&D Innovation Choices

Data lakes contain vast amounts of information, which can be very helpful for research and development teams to verify and test hypotheses and evaluate results.

Deploying Data Lakes in the Cloud

The cloud provides scalability, security, speed, elasticity, availability, reliability, and a diverse set of analytics tools. Hence data lakes are ideal for cloud deployment to run their business-critical analytics workloads. Several companions offer cloud-based data lake services, for e.g., Microsoft Azure Data Lake and AWS amongst others.

Azure Data Lake Storage

Azure Data Lake makes it easy for data analysts and developers to store data and process it across platforms and languages. Azure Data Lake simplifies data management and predictive analysis. It solves productivity and scalability challenges by providing insights into existing data assets and maximizing their value for meeting business needs.

Amazon Web Services

AWS provides secure, scalable, and cost-effective services. Apply data science analytics methods to your data stored on your cloud-based data lake. Organizations like Netflix and the NASDAQ run their data lakes and analytics on AWS. AWS provides a range of managed services for data lakes and analytics.

LakeFS

LakeFS provides version control for data lakes, just the way git provides version control for code management. It provides several features like parallel pipelines, devops support and works seamlessly with latest frameworks like Databricks, Kafka, etc.