Big Data

Big Data

Efficient platform to handle big data and gain valuable insights with less effort, time, and money

Key benefits

Quick project start

Quick project start

Launch a big data project without capital investments in hardware, scale the platform quickly, and pay as you go
Faster decision-making

Faster decision-making

Get analytical reports dramatically faster and make data-driven business decisions
Quick hypothesis testing

Quick hypothesis testing

Test hypotheses and optimize business processes within two weeks after the project start
Business optimization

Business optimization

Gain up to 60% in cost savings with business optimization based on big data as achieved in already completed projects
Reduced maintenance costs

Reduced maintenance costs

Free up IT team from routine tasks by engaging provider’s resources and enjoy up to a threefold reduction in maintenance costs
Strict SLA

Strict SLA

Rely on big data platform and infrastructure uptime with 99.95% SLA and just ten-minute incident response

Use cases

Banking

  • Customer churn prediction and prevention
  • Up to 99.6% accurate forecasting of cash needed at cash desk and ATM
  • Next best action recommendations for front office and contact center agents to increase sales
  • Anti-fraud measures to identify the risk of fraudulent transactions

Manufacturing

  • Defect prediction, prevention, and root cause identification
  • Equipment wear and depreciation monitoring (MRO) for cost-effective maintenance
  • Optimized consumption of expensive raw materials
  • Satellite and aerial imagery to enable geological exploration
  • Optimized and reduced extraction costs

Retail

  • Assortment planning and stock optimization
  • Product recommendations based on check and purchase history
  • Instant analysis of online shopping behavior and reduced cart abandonment rate
  • Omnichannel customer communications with personal touch
  • Supplier ranking

Transportation and logistics

  • Change of rates depending on fleet status, fuel consumption, and busy hours
  • Vehicle telemetry analysis to reduce accidents
  • Fleet schedule management

What we do

Step 1
Audit
Survey your business and IT environments and create a project roadmap
Step 2
Design
Create a data platform architecture in the cloud
Step 3
Installation
Complete installation, testing, and initial master data upload
Step 4
Analytics
Configure analytics and reporting functions
Step 5
Support
Maintain and support the solution 24/7
Step 1
Audit
Survey your business and IT environments and create a project roadmap
Step 2
Design
Create a data platform architecture in the cloud
Step 3
Installation
Complete installation, testing, and initial master data upload
Step 4
Analytics
Configure analytics and reporting functions
Step 5
Support
Maintain and support the solution 24/7
FAQ
How to monetize the existing data?
The phrase «data is the new oil» was coined for a reason, since it has the power to boost business efficiency.

Manufacturing, for instance, uses industrial analytics solutions to check the efficiency of either an entire technological process or production process of a specific division. Data sources (workshops) are too numerous and it is big data analytics that can help study and scrutinize a production process in all respects.

Banks rely increasingly on big data to offer banking products to potential clients. Retailers leverage big data to draft assortment matrix and generate other offerings.
What’s the difference between a data lake and big data?
Data lake is just a big data fraction designed to collect various data, whether structured or not. There is usually no way to sort out unstructured data before saving it, and this is the key feature of data lakes: the data is saved first, and only then handled. Thus, data lakes allow you to «save it for later» and further figure out how to use and integrate data from the lakes into the infrastructure.
What’s the difference between a data lake and storage?
Data lakes store any sets of data, even duplicated ones, and the single version of the truth is created after saving. A data storage, however, keeps structured information without duplicates from the start, thus always having that single version of the truth.

Furthermore, storage is created in compliance with routine processes, so all information is uploaded there only from verified sources under IT team supervision. In contrast, any authorized employee can save whatever information in a data lake, process it, and prepare data marts.

To wrap this up, the difference between those two is in the types of data, load types, application, work patterns, and user involvement in data processing.
Enterprises often use multiple data collection and processing systems. Is it necessary to unify data prior to implementing a big data platform?
Data unification is a common routine. Big data projects often start with data being consolidated on a single analytics platform. One and the same object might look different in different accounting systems; therefore, unification — bringing an object to a single target format — is a common case for big data projects. To do so, different tools can be used, depending on existing data formats and the extent of legacy systems integration.
What systems does the big data platform include?
The functional architecture of the platform has two core subsystems. An integration subsystem collects and imports data and supports data stream and batch processing, while a data storage and processing subsystem offers different storage tiers.

It also includes a BI platform to generate analytical and custom reports, mathematical models, etc. The platform can underlie a data lab that provides users with resources and access privileges to work with data and test hypotheses, including the use of Python.

A data governance subsystem is implemented with the platform to ensure transparency of data sources and data collection processes, as well as that of data transformation for data marts, which improves user awareness and data visibility. Various systems, such as Business Process Management (BPM), can be connected to the platform.
What tools are used to handle big data?
The big data platform employs Arenadata Streaming based on NiFi and Kafka as part of the streaming data integration subsystem. NiFi is also often used as an orchestrator that runs and executes various data processes.

To extract, transform, and load (ETL) data, we employ NiFi, Airflow, and PXF, a framework for Arenadata DB Greenplum, but any tools integrated into the user infrastructure are OK for ETL tasks.

Hadoop is used as part of the data storage and processing subsystem to deal with arbitrary data. A trending Arenadata DB Greenplum is used to store data.

We’ve used Greenplum long enough (since 2014) to be sure that it suits both big data solutions and standalone warehouses. Moreover, Greenplum and Hadoop fit together to ensure data exchange.

Arenadata QuickMarts use ClickHouse. For example, to get a quick response on a large data set, you can save such a set in ClickHouse or prepare data in Greenplum at the Hadoop level and send it to ClickHouse.
Online meetup recording
Big Data in CROC Cloud: Tools  & Practice
01

CROC Cloud May Update

You can now create snapshots from volume versions, increase volume size in Kubernetes and filter resources by main parameters.
02

Automatically scale with the new Auto Scaling Groups

This new service allows you to automatically adapt to load changes by adding or deleting instances in a few minutes.
03

Introducing Launch Templates and Related API Methods

Launch templates streamline the launch of instances of the same type and minimize the risk of configuration errors when deploying them.

04

Volume Versions, а New Feature of CROC Cloud

CROC Cloud introduces a new feature, Volume Versions, that you can use to restore your volume content instantly to the original disk.
05

Integrated database monitoring and other CROC Cloud updates

We would like to share our latest updates and some immediate plans.
06

Ansible Dynamic Inventory support and other update of CROC Cloud

You can now deploy Kubernetes clusters version 1.20.9 in the CROC Cloud. 
Any questions?
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About CROC Cloud Services

CROC Cloud Services is a standalone CROC business unit that offers cloud and managed В2В services.
24/7
10-minute SLA
12 years
in the cloud market
750+
customers across various industries
№1
in cloud service quality (Cnews, 2020)
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