5 Big Data Challenges and How AWS Can Overcome Them

Unlocking the potential of big data is undoubtedly crucial for any modern organization striving for success. The abundance of valuable insights that big data holds regarding consumer behavior and the ability to enrich customer experiences, reduce expenses, drive revenue growth, and foster product development is undeniable.

However, managing big data poses intricate challenges that demand meticulous attention and expertise. Analyzing extensive volumes of data can be a daunting task, but it is not insurmountable.

In this blog, we explore six prominent big data challenges and delve into how Amazon Web Services (AWS) offers solutions to overcome them. By leveraging the power of AWS, organizations can navigate the complexities of big data management and maximize its potential for unparalleled success.

1. Data Growth

We keep hearing that data is growing exponentially, and the statistics bear it out. A Forbes article reported that from 2010 to 2020, the amount of data created, captured, copied, and consumed in the world increased from 1.2 trillion gigabytes to 59 trillion gigabytes. Meanwhile, IDC noted that the amount of data created over the next three years will be more than the data created over the past 30.

That’s a lot of data that may be beneficial for organizations. But it requires a lot of work to extract value from it. This includes storing it, and data storage isn’t free. Migrating existing servers and storage to a cloud-based environment can help, along with solutions such as software-defined storage and methods such as compression, tiering, and deduplication to reduce space consumption.

2. Data Integration

From social media pages, emails, and financial reports to device sensors, satellite images, and delivery receipts, data can come from just about anywhere. Some of it may be structured. Some of it may be unstructured. And some of it may be semi-structured. The challenge for companies is to extract the data from all the various sources, make it all compatible, and provide a unified view so it can be analyzed and used to generate insightful reports.

Many data integration techniques can be utilized for data integration. Same for software programs and platforms that automate the data integration process for connecting and routing data from source systems to target systems. Data integration architects can also develop customized versions.

Selecting the most appropriate data integration tools and techniques requires identifying the ones that best match your integration requirements and enterprise profile.

3. Data Synchronization

Gathering data from disparate sources means that data copies may be migrated from different sources on different schedules and at different rates. The result: they can easily get out of sync with the originating systems, making it difficult to generate a single version of “truth”, and leading to the potential for faulty data analysis.

Trying to repair the situation slows down the overall data analytics endeavor. That can degrade the value of the data and analytics because the information is typically only worthwhile if it is generated promptly.

Fortunately, there are a variety of techniques for facilitating data synchronization. Numerous services can automate and accelerate the processes. The best among them can also archive data to free up storage capacity, replicate data for business continuity, or transfer data to the cloud for analysis and processing.

Built-in security capabilities, such as encryption of data-in-transit, and data integrity verification in transit and at rest, are must-haves. The ability to optimize network bandwidth use and automatically recover from network connectivity failures are pluses too.

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4. Data Security

Big data isn’t just valuable to businesses. It’s a hot commodity for cybercriminals. And they’re persistent – and often successful – in stealing data and using it for nefarious purposes. As such, it can be a privacy issue, as well as a data loss prevention issue and downtime mitigation issue.

It’s not that organizations don’t think about securing data. The problem is they may not fully understand that it requires a multi-faceted, end-to-end, and continually updated approach. The focus must be as much on dealing with the aftermath of a data breach as on preventing one. This includes everything from the endpoints where data originates to the data warehouses and data lakes where it’s stored, to the users that interact with data.

Among the tactics that should be included in a comprehensive data security strategy:

Data encryption and segregation
Identity and access authorization control
Endpoint security
Real-time monitoring
Cloud platform hardening
Security function isolation
Network perimeter security
The use of frameworks and architectures that are optimized for securely storing data in the cloud

5. Compliance Requirements

Regulatory mandates, industry standards, and government regulations that deal with data security and privacy are complex, multijurisdictional, and constantly changing. The sheer amount of data that companies must gather, store, and process ─ resulting in data pipelines and storage systems that are overflowing with data ─ makes meeting compliance requirements especially difficult.

The first step is to stay on top of all current and relevant compliance requirements. Enlist outside specialists if necessary.

Data-related compliance requires the use of reliable, accurate data. Automating and replicating processes can help ensure that the analyzed data meets this criterion, while also facilitating on-demand reporting. Other helpful tactics include the use of compliance and governance frameworks that can connect multiple systems across an organization to create a consistent, auditable view of data regardless of where it resides. In addition, centralized data pipeline management can help simplify governance.

6. Lack of Skilled Personnel

Another major challenge that businesses encounter in harnessing the power of big data is the scarcity of skilled personnel. Big data analytics requires a unique set of skills, including data science, statistics, programming, and domain expertise. However, there is a significant shortage of professionals with these specialized skills. That makes it difficult for businesses to effectively analyze and derive insights from their data. This scarcity creates a bottleneck in organizations’ ability to leverage their data for strategic decision-making and innovation.

To address the challenge of a skilled personnel shortage in big data, businesses can invest in training programs, workshops, and certifications to equip their employees with the necessary skills. Another approach is to leverage external expertise by partnering with experienced cloud professional services firms. This allows organizations to tap into the knowledge of experts without the need for long-term commitments or expensive new hires.

AWS Solutions for Big Data Challenges

At ClearScale, we’ve found that working with AWS data analytics services can help overcome these six big data challenges.

There are benefits associated with the AWS cloud itself, like pay-as-you-go cloud computing capacity and secure infrastructure. There’s also the vast array of compliance resources detailed here.

Additionally, there is a robust portfolio of cloud services to ingest, synchronize, store, secure, process, warehouse, orchestrate, and visualize massive amounts of data.

The following are just a few of the many beneficial services:

Amazon Athena is a serverless query service that simplifies data analysis for information stored in Amazon S3. It doesn’t require setting up or managing any infrastructure. And you don’t have to manually load data for evaluation.
AWS Deep Learning AMIs provide infrastructure and tools to accelerate deep learning in the cloud at any scale. It’s easy to quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks and interfaces such as TensorFlow and Keras to train custom AI models, experiment with algorithms, or learn new techniques.
AWS Glue is a serverless extract, transform, and load (ETL) service that takes on much of the backend work associated with cleaning, enriching, and moving data. As a managed service, it minimizes the complexity of managing ETL jobs. Users only pay for computing resources used while jobs are running.
AWS Lake Formation enables setting up secure data lakes quickly to store processed and unprocessed data. It allows for combining information from different data sources to make better business decisions.
Amazon Redshift is a petabyte-scale data warehouse service for running queries on structured data. It’s three times faster and half the cost of many cloud data warehouses.
Amazon SageMaker enables data scientists and developers to quickly build, train, and deploy machine learning models. It comes with a catalog of models and allows users to implement their own models.

You can learn more about AWS’s big data and analytics services here.

ClearScale Helps Resolve Big Data Challenges

Working with ClearScale offers advantages as well. This includes our extensive experience with AWS services, highlighted by our AWS Data & Analytics Competency. There’s also our long list of successful data and analytics services projects. These range from deploying MLOps programs to automating complex analytical processes to configuring data lakes. Read some of them here:

Whatever your big data challenges or needs are, ClearScale is ready to help.

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