Big Data Processing

99% of Firms Actively Invest in Big Data Initiatives

NewVantage Big Data and AI Survey revealed that 99% of mid-size and large companies already use big data and 92% of them are planning to accelerate their investments in the coming years.

Traditionally, big data solutions are analytics-focused and aimed at driving informed decision making. Though the share of operational big data solutions is steadily growing in line with the need to process petabytes of XaaS users' data or enable IoT-driven automation.

Popular big data processing use cases:

• Real-time vehicle tracking; traffic management; geofencing.
• Medical IoT.
• Credit card fraud/account takeover detection.
• Real-time stock market quotes management.
• Automated real-time anomaly recognition for manufacturing/oil&gas industries.
• Connected smart appliances.
• Online video games.
• XaaS

Big Data Processing: The Essence

Big data processing covers collecting, storing, and managing massive amounts of data (mostly in a semi- or unstructured form) that arrives from multiple sources. Big data processing stages include data ingestion into a data lake or a stream processing engine, data cleansing and transformation, and data loading into an analytics storage optimized for querying and reporting. Processed big data is used to derive insights (including real-time) and trigger immediate automated actions.

Big Data Processing Architecture Options

ScienceSoft explains two architecture options that perfectly meet the needs of the majority of companies:

1. Lambda architecture

Pros:

• Existing ETL processes can be used as the batch layer.
• High performance.
• A low possibility of errors even if the system crashes, as a separate distributed storage will keep historical data intact.
• Fewer data streams with indefinite time-to-live, thus, cheaper PaaS and IaaS services.
• Lower development cost since there's no need to rewrite algorithms (not all algorithms can be made streaming).

2. Kappa architecture

Pros:

• Easy to test and maintain. Only one set of infrastructure and technology is used.
• Data is easy to migrate and reorganize.
• Easy to add new functionalities and make hotfixes (since only one code base should be updated).
• High data quality with guaranteed data sequence and no mismatches.
• Lower infrastructure cost (storage, network, compute, monitoring, logs) since only one tech stack is used and data needs to be processed only once.

Note: Building a big data processing solution is more than just connecting ready components. The share of custom code to integrate different components, support unique operations or create a competitive advantage is still huge and vital.

ScienceSoft: Building Data and Big Data Solutions for 36 Years

• In custom software development since 1989.
• In data management, data analytics and data science since 1989.
• In business intelligence and data warehousing since 2005.
• In big data services since 2013.
• We are proud of our professional and dedicated team of senior project managers, business analysts, solution architects, developers, data analysts, and other IT professionals with 7-20 years of experience.

How ScienceSoft Can Help On Your Big Data Journey

ScienceSoft engineers software solutions that help companies successfully handle the ever-growing amount of data for operational and analytical needs – sensor data, XaaS data, customer and personalization data, images and video, clickstream data, financial transactions data, health data, and more.

With an in-house PMO and mature project management practices, we can handle initiatives of any complexity and drive the project to its goals regardless of time and budget constraints.