Adanto Powers Big Data Democratization for Robert Half

Adanto delivers a cloud-based Big Data Lake solution for a Silicon Valley consulting leader, eliminating data silos, reducing costs, and enabling seamless access to raw data. The solution fosters a data-driven culture and empowers users with scalable, flexible analytics.

Key Results

$2.6M

Annual Cost Savings from IT infrastructure optimization and automation & efficiency

$1M+

Annual productivity gains from time & effort savings and faster decision-making

$500k+

Business Enablement gains from improved SLA compliance & new insights, innovation & agility

Services performed

  • Data Science
  • Data Analytics & Business Intelligence
  • Data Warehousing
  • Big Data
  • Machine Learning
  • Artificial Intelligence
  • DevOps
  • Security
  • Infrastructure Services
  • Salesforce
  • Amazon Cloud
  • Azure Cloud

Technologies used

Data Sources/Silos

  • 60+ data sources
  • 200+ GB of new data per day
  • One Data Store (Data in different AWS data stores based on data type)
  • Amazon S3
  • Amazon EC2
  • Amazon Redshift (data warehouse for standard SQL queries & BI tools)
  • Amazon RDS (relational database for many instance types)
  • Apache Sqoop (O/S tool for bulk data transfers)
  • Amazon HDFS (Parquet) (Hadoop Cluster with EMR – Elastic MapReduce)

Query Tools & Analytics

  • Apache Hive, Pig, Spark (O/S database query interface tools to HDFS & processing engine)
  • R (O/S statistical programming language for data mining and statistical computing)
  • Mahout/scikit-learn (O/S tools for building Machine Learning apps)
  • QlikView, PowerBI, SAS (data analytics, business intelligence and reporting tools)

Challenge

Robert Half was challenged with lack of easy access to company’s enterprise data. The company faced multiple challenges for which it was seeking a solution:

  • Limited agility and accessibility for data analysis.
  • Data silos preventing effective information sharing.
  • High costs due to server and license proliferation and IT complexity (shadow IT)
  • Expensive scalability and lack of flexibility for new systems.

Key goals

1 in circle

Create a centralized repository for raw data accessible across departments

2 in circle

Implement incremental load processes and data governance procedures

3 in circle

Develop thematic, departmental, and business line-focused data marts

4 in circle

Build analytic applications tailored to specific business needs

Solution

Big Data Lakes are enterprise-wide data management platforms that store disparate data sources in their native format until queried for analysis. Unlike purpose-built data stores, data lakes consolidate raw data in its original form, eliminating information silos and enabling better data sharing. This approach reduces server and licensing costs, provides scalable and flexible storage, and ensures data accessibility for both programmers and business users

Adanto implemented a scalable and cost-effective cloud-based data lake infrastructure:

  • Stored data in Amazon S3 Buckets for cost efficiency.
  • Utilized parquet file format with HDFS/Hive for structured querying.
  • Established a Hadoop/Spark cluster in AWS with autoscaling capabilities.
  • Set up incremental data load processes using Apache Sqoop on an EMR cluster for daily data ingestion.

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