Big Data Infrastructure: Analytics Guide for 2023

There’s been a widespread misunderstanding that data automatically translates to razor-sharp insight. The truth is, the sheer amount of data being generated on a daily basis has left businesses with a gargantuan task – one survey found that 78% of data analysts and engineers felt their company’s data was “growing faster than their ability to derive value from it.” 

Among the top-cited reasons for this dilemma was a poor or outdated data infrastructure. So, how can businesses set themselves up to better handle this influx of big data? 

Understanding big data infrastructure

Big data infrastructure encompasses the tools and technologies that enable an organization to collect, store, transform, manage, and activate massive amounts of data. This infrastructure is necessary for running analytics and applications that rely on many gigabytes, or even petabytes, of data. Think inventory tracking by omnichannel retailers, fraud detection by banks, algorithm-based recommendations by social apps, and personalized retargeting by advertising platforms.

Tools and systems in big data infrastructure include:

• Storage systems – e.g., data lakes and cloud-based data warehouses

• Integration and transformation tools – e.g., ELT pipelines, NoSQL databases, migrators

• Interfaces – e.g., query engines, APIs

• Analytics and activation tools – e.g., business intelligence software, customer data platforms (CDPs)

Why is big data infrastructure important?

Big data infrastructure enables organizations to operationalize structured, semi-structured, and unstructured data that they gather every day. With analytics software and centralized databases, you can use data-driven insights to guide decisions.

For example, a nationwide grocery chain requires sales data from their POS systems both in physical stores and e-commerce transactions at the close of every business day. This real-time inventory data helps them update product availability, leading to better shopping experiences. The grocery chain can also use this data to analyze supply and demand (e.g., seasonal patterns), make forecasts, detect anomalies, personalize marketing campaigns, and use external data signals to identify emerging market trends.

All this information can be overwhelming without infrastructure designed to capture, organize, validate, and analyze the data. In fact, many businesses find themselves drowning in data and are seeking better ways to link it with systems that unearth useful insights. This starts with the design of your core big data infrastructure.

The core components of big data infrastructure

Big data infrastructure lets you process data at a massive scale and low latency. This is typically done through distributed processing. When building your big data pipeline, start with the following processing technologies:

Hadoop

Hadoop is an open-source framework for storing and processing massive datasets. It uses clusters of hardware to store and process data efficiently. Hadoop has three main components:

• The Hadoop Distributed File System (HDFS) splits data into blocks, which are distributed across many computers that act as nodes in a system. For fault tolerance, HDFS makes copies of the data blocks and stores them on different nodes.

• MapReduce is a software framework for writing applications that split data into parts and process those parts separately across thousands of data nodes. Processing is done in parallel across node clusters.

• Yet Another Resource Negotiator (YARN) is a resource management layer that sits between the HDFS and data processing applications. It reduces bottlenecks by allocating system resources, enabling multiple processing job requests to be done simultaneously. YARN works with batch, stream, graph, and interactive processing systems.

Factors like elasticity, fault tolerance, and low latency have made Hadoop a popular framework for applications that use real-time data generated by a huge user base, such as Facebook Messenger.

Massively parallel processing

Massively parallel processing (MPP) is a processing paradigm where computers collaboratively work on different parts of a program or computational task. An MPP system typically consists of thousands of processing nodes (computers or processors) working in parallel. 

The compute nodes communicate through a network and are assigned work by a leader node. Each node has its own operating system and independent memory and processes a different part of a shared database. Unlike in Hadoop, MPP doesn’t replicate datasets across different nodes.

MPP architecture speeds up data processing by distributing processing power across nodes. It also allows for efficient computing when different people run queries at the same time. These attributes help explain why MPP is the underlying architecture of choice for data warehouses like Snowflake, BigQuery, and Amazon Redshift. It also supports SQL-based business intelligence tools like Tableau.

NoSQL

NoSQL is a non-relational database system that allows for distributed data processing. Unlike SQL, NoSQL doesn't require a fixed schema or specific query language. This flexibility allows NoSQL-based platforms to work with structured, semi-structured, and unstructured data from many different sound devices. They can scale rapidly thanks to their schema-agnostic design and distributed architecture.

NoSQL databases are open-source and designed to work across large clusters. MongoDB, Cassandra, and Neo4J are examples of NoSQL databases and database management systems.

3 big data infrastructure challenges

In designing your big data infrastructure, you may find that you need to make tradeoffs. Prioritizing system availability by choosing a distributed network can mean giving up the high data consistency offered by relational databases. Capturing data streams from multiple sources without bottlenecks caused by fixed schemas can also mean dealing with issues like duplicate data. Maximizing fault tolerance can slow down a system.

Aside from choosing your priorities, consider the challenges presented by your big data system design and how you can resolve them (or at least make them rare occurrences).

Sluggish data processing 

Several factors may cause slow data processing in a big data pipeline. One node in your system may have a poor network connection, or your network architecture may lack intelligent, programmatic efficiency. You may have also designed your system in a way that increases latency – for example, raising the replication factor in HDFS improves data availability and fault tolerance but also increases the network’s load and storage requirements.

For applications that require real-time data, low latency is a non-negotiable requirement. Think smart traffic systems, recommendation engines within an e-commerce app, and fraud monitoring systems for credit card companies.

Complex data reformatting

There’s no universal rule on how devices and software format their data types and properties, which means the same event can be represented differently if you don’t specify a fixed schema.

For example, a purchase may be given an OrderComplete format in one database and order_complete in another, resulting in one event being counted as two. Repeat such discrepancies across thousands of datasets, and your data reformatting process becomes tedious, complex, and time-consuming.

Undetected faults

The distributed nature of big data tools increases the possible points of failure. In an MPP system, for example, faults can often go undetected due to the sheer number of nodes involved. Separate software is needed to implement a fault-tolerant layer. 

While isolated faults may not have a noticeable impact on your system, repeated undetected failures can slow down a system or prevent a node from moving on to the next processing task. They can result in incomplete data, system bottlenecks, and increased computing costs. 

Twilio Segment: How centralized data drives results

CDPs like Twilio Segment can enhance your big data infrastructure by collecting, cleaning, and consolidating data at scale, and in real time. Here are some use cases:

Collect, unify, and connect customer data with Connections

Twilio Segment's Connections API lets you collect and centralize data across devices and channels without building custom integrations. It requires a fixed schema, which you can easily apply by using tracking plans – specs that define the data events and properties you intend to collect. Twilio Segment flags data that doesn't conform to your tracking plan, so you can reformat it with quick clicks or apply automatic reformatting rules.

Ensure data quality and security with Protocols

Protocols is a Twilio Segment feature that automates and scales data governance and data quality best practices. It flags duplicate, incorrect, and incomplete data, as well as data that doesn't comply with privacy and security requirements. That means even though dirty data may slip through your big data system, Twilio Segment can catch it, preventing you from making decisions and running workflows based on poor-quality data.

Launch personalized marketing campaigns based on real-time data with Twilio Engage

Twilio Engage is a customer engagement platform built on top of Twilio Segment CDP. This tight integration means Twilio Engage can rapidly pull customer data, preventing sluggish processing and updates.

For instance, when a customer shops on an e-commerce site, the CDP gathers real-time data on the products viewed and bought. These actions are added to the customer's profile on the CDP. Any marketing workflow on Twilio Engage that uses that customer's profile gets the updated version.