Big Data Analytics + Data Warehouse = More Informed Decisions

Big data is a massive amount of intelligence that is too hard to process with conventional databases and processing software. Big data architecture allows organizations to perform business analytics on data stored in various applications, regardless of format. A data warehouse is a collection of info from heterogeneous sources. The data is transformed and uploaded to a repository where it can be analyzed and manipulated to provide business insights. But how to properly set up a warehouse for high-quality big data analysis?

Research and Markets predicts a 13% CAGR in global big data spending from 2017 to 2027

Relationship Between Data Warehouse and Big Data Analytics

Data warehouses and big data analytics are closely related but serve different purposes. Schedule a call to complement reality with a profitable solution.

What is the difference?

A data warehouse stores structured data collected, cleaned and transformed for analysis. It is typically used for business intelligence and reporting applications and is designed to support complex queries and analysis of historical data. In contrast, big data analytics involves processing and analyzing large volumes of unstructured or semi-structured data to extract insights and trends.

Two benefits

Big data analytics can benefit from data warehouses in two main ways.

1. Data warehouses can produce a single source of truth for the data used in big data analytics. By integrating data from numerous sources into a data warehouse, businesses can ensure that the data is clean, standardized, and consistent, improving the accuracy of their analysis.

2. Data warehouse produces a structured environment for big data analytics. While it often involves processing unstructured data, data warehouses can provide a structured environment for organizing and storing the results of big data analytics.

The sheer volume of generated business data and the need to provide real-time analytics pushes brands to favor big data solutions.

What has to be overcome

There are several challenges faced in a data warehouse for big data analytics:

• Traditional data warehouse systems are not designed to handle the many volumes of data generated by big data analytics. The collected data can cause performance issues, leading to delays and even system crashes.

• Data from various sources must be integrated into the data warehouse for analysis. But big data often comes in different formats and structures, which makes integration difficult.

• Big data is often unstructured and of varying quality, so it is challenging to clean and standardize it for analysis. The feature can result in inaccurate insights and recommendations.

• As the quantity of data stored in the data warehouse grows, so does the potential risk of security breaches. It is vital to have decisive data security measures to protect the data from unauthorized access.

Integrating a data warehouse as one of the big data analytics tools means overcoming these challenges.

Problems start with the letter V

Different sources cite significant challenges when creating a data warehouse for big data analysis. But they all start with the letter V and have to do with the relationship of the received data with analytics tools. With the advent of big data, its parameters have increased significantly, which can overwhelm traditional data warehousing systems designed for more manageable volumes of data.

Volume: managing large amounts of data

Managing large volumes of data presents several challenges

• The sheer volume of data exceeds the capacity of the traditional data warehouse.

• Managing large volumes of data requires a distributed processing framework that scales horizontally across multiple servers.

• Big data often comes in different formats and structures, making integrating with existing data warehouse systems difficult.

• Monitoring the quality of data becomes challenging as the volume of data increases.

Organizations must invest in modern data warehouse systems designed for big data analytics to manage large volumes of data.

Velocity: the faster, the better

Velocity brings up the speed of data generation and processing. Data velocity has increased dramatically with the evolution of the Internet of Things and other real-time sources.

Customers use in-memory processing and distributed computing to manage data velocity. They also use data ingestion tools like Apache Kafka to capture and store data in real-time. These systems leverage advanced analytics techniques like the stream and complex event processing to derive meaningful insights.

Variety: reducing to a common denominator

Variety introduces the different types and formats of data brands collect, including structured, semi-structured, and unstructured data.

• Data may be stored in disparate formats, making integrating with existing data warehouse systems difficult.

• Unstructured data (text and images) can take up significant storage space.

• Traditional data warehouse systems are not designed to handle unstructured and semi-structured data.

• Unstructured data may contain incomplete, inconsistent, or inaccurate data, affecting the accuracy of insights derived from data analytics.

Modern data warehousing uses flexible and scalable storage solutions, such as Hadoop Distributed File System or cloud-based storage. These systems leverage advanced analytics techniques, such as natural language processing and image recognition.

Veracity: you can rely on data

Ensuring veracity has become more challenging with the increasing volume, velocity, and variety of data. Data warehouse systems operate with data quality tools and governance policies to ensure data is collected, stored, and used according to established standards. They also apply advanced security measures to protect sensitive data from unauthorized access. Such systems support explainable artificial intelligence (AI) development to address bias and ethical concerns.

Another word for the letter V

Complex data structures mean the data has multiple layers of relationships, such as hierarchical or graph-like structures.

• Processing complex data structures requires specialized techniques not typically found in standard data warehouse systems.

• Analyzing in such a case needs to use machine learning, deep learning, and graph analytics, which can handle the complexity of the data.

• Traditional data visualization practices need to be revised to display the complex relationships within the data.

There is another word for the letter V — value. It is determined by the benefits that the company can derive from the data it collects. It is an aggregate factor summed up by all or some of the characteristics listed above: for example, veracity, velocity, and variability. If you think this is your case, then arrange a call.

What Data Warehousing Needs for Data Analytics

The primary sources of Big Data are:

• Internet of Things (IoT) devices

• social media

• Internet services (service portals, online stores)

• equipment and devices of various types

• medical and social organizations

Modern computing power allows one to access data instantly since it’s stored in data centers on servers with modern components.

A couple of essential concepts

Distributed systems and parallel processing enable warehouse systems to handle large amounts of data and complex analytical queries.

1. Distributed systems are a collection of computers or nodes that work together to perform a specific task.

2. On the other hand, parallel processing involves dividing an enormous task into mini-tasks that can be executed simultaneously.

There are several tools available for distributed systems and parallel processing:

• Hadoop is an open-source framework. It provides a distributed file system (HDFS) and a distributed processing engine (MapReduce) that can be used for big data analytics.

• Open-sourced Apache Spark provides an in-memory processing engine and supports parallel processing for improved performance.

• Apache Kafka is an open-source, distributed streaming platform for real-time data ingestion and processing.

• Amazon Redshift is a cloud-based data warehouse service that provides scalable storage and processing capabilities for large data sets.

• Cloud-based Google BigQuery supports SQL queries and integrates with various data sources.

These tools and technologies can be combined to build distributed data warehouse systems.

In a column, not in a row

A columnar relational database is a management system that stores data in columns rather than rows. Each column is stored separately in a columnar database and can be compressed and indexed independently. This type of database is optimized for analytical queries and data warehouse applications, as it enables fast and efficient querying of large datasets.

Some popular columnar databases for data warehousing and big data analytics include Amazon Redshift, Apache Cassandra, and Vertica. These databases are designed to master large volumes of data and complex analytical queries, and they can be used in combination with distributed data systems and parallel processing technologies to build scalable and efficient data warehouse systems.

Stay in memory

In-memory databases are a type of database management system that stores data in the main memory of a computer rather than on disk or other external storage devices. It provides faster access to data and quicker processing of queries, making them well-suited for data warehousing and big data analytics applications.

They are also highly scalable because they can be distributed across multiple servers and handle many simultaneous queries. It gives customer enablement to build large-scale data warehouse systems supporting real-time analytics and high-performance applications.

Several popular in-memory warehousing and big data analytics databases exist, including SAP HANA, Oracle TimesTen, and MemSQL.

Cloud computing data technology

With cloud infrastructure data warehousing, businesses can store and analyze large volumes of data without investing in expensive on-premises hardware or infrastructure and data engineers' learning.

1. With cloud-based data warehousing, customers quickly scale their data storage and processing capabilities up or down as needed.

2. Stored in the cloud, data is easily accessed and analyzed from anywhere using various tools and platforms.

Popular cloud-based data warehouse solutions include Amazon Redshift, Google BigQuery, and Microsoft Azure SQL Data Warehouse. These solutions give a range of features and capabilities, including support for distributed processing, columnar storage, and in-memory processing.

Warehouse virtual reality

Data virtualization is a technology that allows data from multiple sources to be combined and presented to users as a single, unified data source. In data warehousing and big data analytics, data virtualization produces a unified view of data from multiple sources, including structured, semi-structured, and unstructured data. It is accessed directly from the source systems, so users can query and analyze the available up-to-date data without waiting for batch processing or data movement.

The popular data virtualization platforms are Denodo, Informatica, and TIBCO. They yield features and capabilities, including data integration, data transformation, and governance support.

How to Approach Data Warehousing for Big Data Analytics

At the beginning of building a data warehouse for big data analysis by computer science, you need to make many strategic data-driven decisions and choose a construction plan. Depending on the specific case, its points will be different; there are no good or bad ones. You can choose a unique way of building or use the beaten paths — the best practices that have proven themselves over time.

Architectural solutions

When discussing the future data warehouse architecture for big data analysis, the following best practices can be used:

• The data model should be as simple as possible and best defined at design time. The first ETL assignment should only be written after that.

• Using architectures based on massively parallel processing is better because a storage system based on a single instance will be challenging to scale.

• If the usage involves a real-time component, it is better to use the standard lambda architecture, which has a separate layer for this.

• ELT is preferred over ETL in modern architectures if there is no complete understanding of the full specification of the ETL job and there is no possibility of new kinds of data entering the system.

Technical expertise is essential in designing the data models for the warehouse. Following this systematic approach, you can effectively plan, design, implement, and optimize your data warehouse for big data analytics.

Maintain a high level of quality

Ensuring quality and accuracy through data governance and master data management (MDM) is crucial for maintaining reliable and trustworthy data.

1. Data governance encompasses the processes, policies, and guidelines that ensure data availability, usability, integrity, and security. It aims to enforce standards and controls for data management.

2. Master data management is a comprehensive approach to identifying, defining, and managing a critical data entity («master data»). It typically includes customers, products, employees, or other core business data.

By implementing robust data governance practices and adopting effective MDM strategies, customers establish a solid foundation for maintaining data quality.

Increasing the efficiency of the database system

The optimization of database performance involves defining and addressing performance bottlenecks, reducing resource utilization, and enhancing query execution speed to ensure optimal processing. The goal is to achieve faster response times and improved throughput.

• Query optimization includes optimizing joins, filtering conditions, aggregations, and rewriting queries for more efficient algorithms or indexes.

• Indexes facilitate quicker data access by organizing data to allow the database engine to locate specific records efficiently.

• Data partitioning distributes data across multiple physical storage locations, allowing for parallel processing and cutting the impact of data growth on performance.

• Designing a fine-drawn database schema and data model minimizes redundancy, ensures integrity, and supports fast retrieval. NoSQL databases offer a flexible schema design for dynamic and agile data modeling.

• Allocating sufficient hardware resources, such as CPU, memory, and disk I/O, to the database server makes optimal use of available resources.

Data warehouse performance also requires tuning, regular maintenance, and monitoring. DATAFOREST provides such services.

Protect sensitive data at all times

Sensitive data refers to personal data with private information: names, addresses, email, or financial information — a credit card or health insurance number. The process must be in consent with legal restrictions. A company's trade secret is also confidential data. To accurately identify this data type in your geolocation, you must study the relevant legislation, such as the European General Data Protection Regulation. In general, measures to protect confidential data involve the following:

• Access controls and data governance

• Encryption during storage and transmission

• Data anonymization and masking

• Regular security audits and monitoring

• Increasing the level of staff training and awareness

By implementing these measures, bodies can mitigate risks, maintain the integrity of sensitive data, and comply with relevant regulations about data protection.

Exploring the data by yourself

Providing easy and intuitive access to data through data mart visualization with nice front-end and self-service analytics tools enables users to explore and analyze data independently without requiring extensive technical skills or assistance from data experts. This approach empowers individuals across an organization to access, interpret, and derive insights from data using user-friendly interfaces and interactive visualizations.

There are some popular options: Tableau, Microsoft Power BI, QlikView and Qlik Sense, Looker, and Google Data Studio.

Source of inspiration and insights

The key drivers for Big Data Strategy

To get to the valuable information for business obtained after processing and analyzing data, you first need to jump over several barriers: scalability (depends on data engineering), data integration, quality, storage details, and processing, security, and privacy detail. You also need to be aware of the desirability of real-time analytics and the need for skilled data science workers.

Instrumental help

Here are some tools to help you overcome the challenges:

• Traditional data warehouse architectures may need help to cope with the scale and performance demands. Implementing distributed computing frameworks like Apache Hadoop or Apache Spark provides horizontal scalability by processing across machine clusters.

• Data integration challenges arise due to formats, schema, and quality differences. Realizing data integration techniques such as ETL or ELT processes helps to consolidate and transform data into a unified form suitable for analysis.

• Utilizing distributed file systems like Hadoop Distributed File System (HDFS) or object storage systems like Amazon S3 can handle the storage needs. Distributed processing frameworks like Apache Spark or Apache Hive process data in parallel to achieve faster analytics performance.

• Big data analytics demand real-time or near-real-time processing to enable timely decision-making. Stream processing frameworks (e.g., Apache Kafka, Apache Flink) or in-memory databases solve the problem by handling high-velocity data streams and enabling faster processing.

Addressing these challenges in data warehouses for big data analytics requires a combination of information technology solutions, data management practices, and skilled resources.

The right tools do the right thing

Selecting the right tools and technologies based on specific business needs in data warehousing for big data analytics. If there is an obstacle in the path of the seedling, the tree will grow crooked. The choice of tools affects the essential characteristics of the process: data efficiency, scalability, and integration. At the analysis stage, this can lead to errors in analytics, future-proofing, or cost optimization. If you need help figuring it out independently, some companies provide third-party professional services in the warehouse building.

What tomorrow brings

DATAFOREST has a long and successful history of providing data science-managed services and monitors trends in the industry to maximize user experience. The industry direction points to three major trends for warehousing data scientists shortly.

1. Cloud-based data warehousing

2. Real-time analytics and streaming data processing

3. Data lakes and data lakehouses as scalable approaches for managing big data

If the topic of warehouses for big data analytics is relevant to you or just interested, please fill out the form, and we will continue the conversation about increasing the opportunities for your business.

FAQ

What is warehousing, and how are its properties beneficial for big data analytics?

Warehousing is collecting, storing, and organizing large volumes of data from various sources into a centralized repository, known as a data warehouse. By leveraging the properties of the warehouse, big data analytics can benefit from efficient data integration, streamlined access to structured data, optimized performance, historical data analysis, and improved data governance. Time series data in the warehouse enables organizations to analyze historical patterns.

What are the difficulties of warehousing in providing information for big data analytics?

Organizations may need help using warehouses to provide information for big data analytics: data volume and velocity, variety of sources and formats, integration complexity, scalability, and cost. It needs to be considered before the launch consent declaration.

What are the basic principles of building a warehouse for big data analytics?

Building a warehouse for big data analytics involves following several basic principles: clear objectives definition, data sources, quality understanding, choosing an appropriate data model, designing for scalability, and data integration.

How can you check the quality of information when warehousing for big data analytics?

Here are several approaches and techniques to assess the quality of information: data profiling, cleansing and standardization, data verification, metadata management, and governance practices.

What is the role of data integration in warehousing for data analytics?

Data integration in the warehouse ensures that disparate data sources are harmonized, transformed, and aligned to provide a consistent and accurate representation of the organization's data, enabling meaningful insights and actionable business outcomes through data analytics and data mining.

How can a company be sure that sensitive data is safe when warehousing for data analytics?

Here are a few measures that many companies adopt to safeguard sensitive data: classification, authorization, encryption, anonymization, and monitoring. Increasing these measures is the legitimate interest of businesses. Web development is closely linked to data warehousing by providing the means to access, present, and interact with data stored in the data warehouse.

What tools and techniques are most relevant when creating a warehouse for data analysis?

Here are five top tools and techniques that are commonly used and highly relevant:

1. ETL Tools: Informatica PowerCenter, IBM InfoSphere DataStage, and Microsoft SQL Server Integration Services (SSIS).

2. Data Modeling Tools: ERwin, Oracle SQL Developer Data Modeler, and Microsoft Visio.

3. SQL and Querying Tools: Oracle SQL Developer, Microsoft SQL Server Management Studio (SSMS), and Tableau.

4. Business Intelligence (BI) and Visualization Data Tools: Tableau, Microsoft Power BI, and QlikView.

5. Data Quality and Governance Tools: Informatica Data Quality, IBM InfoSphere Information Analyzer, and Talend Data Quality.

While relational databases are prevalent in data warehousing, other technologies, and data storage approaches, such as NoSQL databases, columnar databases, and distributed file systems, are worth noting. The above services accelerate warehousing processes.

What proven practices can be used when building a data warehouse?

You can use some of the key practices to consider: precise business requirements, effective data modeling, robust ETL processes, data quality management, and performance optimization. It is also possible to use unique software development.

What are the business benefits of warehousing for big data analytics?

The top business benefits of warehousing for big data analytics are enhanced decision-making, improved operational data efficiency, comprehensive market research, ameliorated customer data learning and understanding, competitive advantage, and fraud detection. The improvement of a supply chain is also a benefit.

What mistakes should be avoided when deploying warehousing for data analysis?

Organizations can increase the chances of successfully utilizing a data warehousing solution for data analysis by avoiding these common mistakes: inadequate planning, poor data quality management, inefficient ETL processes, failure to consider scalability, and lack of user adoption.