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The 7 Costly and Complex Challenges of Big Data Analytics

Enterprise DevOps teams, SREs, and data engineers everywhere are struggling to navigate the growing costs and complexity of big data analytics, particularly when it comes to operational data. To highlight these challenges - and how ChaosSearch can help - we published a new video series (featuring ChaosSearch CTO & Founder Thomas Hazel) that explores the enterprise data journey along with seven costly and complex challenges of big data analytics:

  1. Data Pipelines
  2. Data Preparation
  3. Data Destination
  4. Data Governance
  5. Data Platforms
  6. Data Analytics
  7. Data Lifecycle


Big Data Analytics


For our blog this week, we’ve compiled summaries of each big data analytics challenge along with a link to the related video and how ChaosSearch can help. Keep reading to learn all about the 7 costly and complex challenges of data analytics, plus how you can better navigate your enterprise data journey with ChaosSearch.


Seven Costly and Complex Challenges of Big Data Analytics


Challenge 1: Data Pipelines


A data pipeline is a series of tools/steps for processing raw data where the output of one step becomes the input for the next step. Data from a source (e.g. an application, machine, etc.) is ingested and travels over a network to a separate computer/application where it may be prepared (e.g. cleaned, transformed, etc.) before it is sent off somewhere else for analysis.

Modern data pipelines fulfill three major purposes in the data journey:

  • Moving data from source (e.g. an application) to destination (e.g. business intelligence tools),
  • Buffering data in case the data is generated and ingested faster than it can be processed at the destination, and
  • Preparing and enriching raw data with specialized tooling prior to analysis.

Data engineers can build and manage data pipelines using software tools like Matillion, Fivetran, and Fluentd, but these tools often run into scalability issues as enterprises generate growing volumes of data: high costs, slow-downs, failing capabilities, and complexity that degrade data quality and increases management overhead for data engineering teams.

READ: Leveraging Amazon S3 Cloud Object Storage for Analytics


Challenge 2: Data Preparation


Data preparation is the act of pre-processing raw data, often from disparate data sources, into a normalized format that can readily be accessed and analyzed by downstream tools. Data preparation happens early in the data journey and can include many discrete tasks like data ingestion, data transformation, data cleansing, and data enrichment or augmentation before the prepared data is sent on to the destination database.

The goal of data preparation is to normalize data into a defined schema so it can be consumed consistently. The exact requirements of data preparation vary greatly and depend on the source data. Some sources use data protocols like JSON to describe events, which can be quite complex and require careful transformations before analysis.

Data preparation can consume up to between 60 and 75% of resources in an organization’s data analytics program, with DevOps teams, SREs, and data engineers spending weeks to set up pipelines and their corresponding data preparation logic. And as organizations continue to generate more data than ever before, the time, cost, and complexity of data preparation is continuing on an upward trajectory.


Challenges 3 & 4: Data Destination and Governance


Data destinations are the storage repositories at the end of the data pipeline, including relational databases, data warehouse solutions, and modern enterprise data lakes. Data governance is all about controlling data security, managing the availability, usability, and integrity of enterprise data, and complying with international regulations and standards like the EU GDPR, HIPAA, SOC2, and others.

Data governance strategy and controls must span the entire data pipeline - from data creation and ingestion, through preparation, and into the storage repository - but the biggest challenges in data governance are localized around the final destination element of the data pipeline.

Many enterprises use relational databases as the final destination for data. These solutions typically have a strong RBAC construct when it comes to access, which is great for most use cases. But some data governance frameworks require a more robust approach where data from different companies or customers is not stored in the same dataset - where data at rest is both controlled and separated, and not just by external APIs. Databases were never designed for this, especially when the scale of data is in the thousands or millions of different topics or identifiers.

Enterprises can leverage data lakes as a true data isolation and governance platform, but moving data out of the data lake (e.g. into downstream analytics tools) is where the time, cost, and complexity of analytics at scale starts to cause pain. Another governance challenge is lifecycle management: when data can or must be expired from the final destination after a set time, significant complexity is added to the underlying data pipeline.

ChaosSearch addresses these challenges with an innovative data lake platform that goes all-in on the capabilities of cloud object storage to deliver an idealized data destination and optimized approach to governance.


Challenge 5: Data Platforms


A data platform (or cloud data platform) is an integrated set of data technologies that together meet an enterprise’s end-to-end data needs, including data storage, delivery, governance, and security layer for users and applications. The heart of a data platform is its underlying database.

Data platforms in the cloud can take several forms, such as a data warehouse (e.g. Snowflake), a distributed query engine (e.g. Presto) where complex SQL is required, a NoSQL platform like Mongo, Cassandro, or Elasticsearch, a data lakehouse like Databricks, or a data lakebase (data lake infused database) platform like ChaosSearch.

Download: Deep Dive on the Cloud Data Platform [Eckerson Report]

When it comes to efficiently storing and analyzing big data, most of today’s data platforms provide a trade-off between generic power and flexibility. Storing data in a relational/SQL database requires a predefined schema that makes it easy to access information in a generic and powerful way, but time-consuming and complex to change the schema model to accommodate new data fields or use cases.

On the other hand, storing unstructured data in a NoSQL database or data lake provides more flexibility and simplicity on ingestion - but the schemaless data almost always needs more processing before analysis, which can lead to degraded query performance and slower time to insights.

To give our customers the best of both worlds, we designed the ChaosSearch platform to deliver “schema-on-write” query performance with “schema-on-read” ingestion flexibility and scale.

Read: Building a Cost-Effective Full Observability Solution Around Open APIs and CNCF Projects


Challenge 6: Data Analytics


Data analytics is the systematic computational analysis of data or statistics used for the discovery and communication of meaningful patterns or insights that can guide decision-making.

Data analytics has traditionally been applied to business intelligence (BI) workloads and use cases, but enterprises today are also capturing and analyzing operational data to manage the security and performance of applications or cloud services that produce business intelligence. There’s now an increasing demand for operational and BI data to be analyzed together, with insights shared across the two systems.

This is where the challenges start in modern organizations where operational and business analytics are run separately by two different departments with big data silos and no unified analytics platform. Instead, we often see the two sides supported by different solutions with different technologies and architectures - for example, we might see operational databases supported by Elasticsearch, BI databases supported by a relational database like Snowflake, and a separate data pipeline tool like Cribl used to move data between them.

Using all of these tools in the absence of a unified data platform adds time, cost, and complexity to data analytics while maintaining data silos that create barriers to developing new insights.

Learn more about how to build a unified cloud data platform that supports native operational and BI workloads with true multi-model data access. (Spoiler: it’s like having a virtual Elasticsearch, Snowflake, and Databricks engine under the hood without the cost and complexity of running three separate solutions)

Read: Sixth Street Breaks Down Silos and Deploys a Streamlined Logging Solution with ChaosSearch


Challenge 7: Data Lifecycle


Data lifecycle management (DLM) encompasses the full data journey from source to destination, including data creation, data collection/ingestion and preparation, data storage, and data usage/analysis. But there’s one aspect that still hasn’t been covered: data expiration, or the eventual need to delete data from existence (and the challenges of managing this process at scale).

When we think about long-term data retention by an IT organization, there are two motivating factors:

  • Retaining data for as long as possible to continuous derive insights and enable long-term analytics use cases, and
  • Retaining certain data for a specified time period (sometimes as much as 7-10 years) that satisfies regulatory requirements for data retention.

Satisfying these two objectives is where challenges start to arise when it comes to managing the data lifecycle at scale.

First, the actual cost to store data for the long-term depends on the platform used for storage and retrieval, so wrong platform = high costs. Enterprises can “archive” data into cheaper storage for long-term data retention, but this data then becomes time-consuming and complex to retrieve, making it virtually non-existent.

On the regulatory side, achieving compliance with data privacy laws like the GDPR is both complex and time-consuming at scale, especially when enterprises are required to maintain data storage systems that protect data security and sovereignty, rights to access, rights for control change, right to be forgotten, etc.

ChaosSearch addresses these lifecycle challenges by transforming your cloud storage (such as Amazon S3) into a hot analytical data lake. In doing so, we’re able to take advantage of cloud object storage capabilities, which include:


Overcome the 7 Challenges of Big Data Analytics with ChaosSearch

We hope this video series sheds some light on the cost, time-consuming, and complex challenges of big data analytics - and how ChaosSearch is helping our customers overcome those challenges by leveraging the power of cloud object storage to drive all aspects of our technology and architecture.

Thanks for joining us on the data journey!


Ready to learn more?

Start a Free Trial of ChaosSearch to instantly transform your cloud object storage into an analytical data lake and start performing cost-effective data analytics on a massive scale.


Additional Resources

Read the Blog: 2022 Data Delivery and Consumption Patterns Survey: Highlights and Key Findings

Listen to the Podcast: Differentiate or Drown: Managing Modern-Day Data

Check out the Brief: How a Cloud Data Platform Scales Log Analytics and Fulfills the Data Lake Promise

About the Author, Thomas Hazel

Thomas Hazel is Founder, CTO, and Chief Scientist of ChaosSearch. He is a serial entrepreneur at the forefront of communication, virtualization, and database technology and the inventor of ChaosSearch's patented IP. Thomas has also patented several other technologies in the areas of distributed algorithms, virtualization and database science. He holds a Bachelor of Science in Computer Science from University of New Hampshire, Hall of Fame Alumni Inductee, and founded both student & professional chapters of the Association for Computing Machinery (ACM). More posts by Thomas Hazel