Confirm your organization oversees monitoring of change initiatives to ensure improvements are achieved as intended and there is a measurable return on investment through transaction reduction or improved customer satisfaction.

More Uses of the Hadoop Toolkit:

  • Lead: closely work with the Hadoop development, infrastructure, network, database, and business intelligence teams.
  • Supervise: Hadoop, NoSQL , ETL, streaming, machine learning or other big data/analytics technology.
  • Identify: for your data analytics platforms, you use a combination of AWS Redshift, Hadoop, and GPU servers.
  • Create charter, scope and forecast budgets of project, alongside its priority, risks and impact.
  • Identify: design, build and deploy algorithms to optimize the performance of advertising campaigns.
  • Facilitate data integration on traditional and Hadoop environments by assessing clients enterprise IT environments.
  • Systematize: Hadoop based platform and migrate the existing data platforms and provide production support.
  • Head: Hadoop, Azure IaaS, high availability, clustering, service resilience and distributed systems.
  • Use technical acumen to provide design alternatives and support team in technical issue resolution.
  • Initiate: Cloudera Hadoop development for data engineering in enterprise data lake that uses Cloudera Hadoop technology.
  • Establish: review, develop, and implement strategies that preserve the availability, stability, security and scalability of large Hadoop clusters.
  • Perform deep dive analysis to understand drivers of marketing performance to shape ongoing and future campaigns.
  • Solidify expertise with big data technologies as Hadoop, Apache Spark, NoSQL databases, etc.
  • Make sure that your business complies; analysis of ETL (data warehouse) along with big data Hadoop automation feasibility.
  • Provide analysis of Hadoop environments and perform the necessary actions to avoid deficiencies and service interruptions.
  • Be accountable for finding architectural and performance bottlenecks, and stable releases which your customers expect.
  • Compose technical plans, Decomposing large scale projects into manageable technical components.
  • Oversee: design, build and support algorithms of data transformation, conversion, computation on Hadoop, spark and other distributed big data systems.
  • Ensure you deliver; build predictive models using machine learning techniques that generate data driven insights on modern data platforms (Spark, Hadoop and other map reduce tools).
  • Pilot: partner closely with Hadoop admin team and other development teams to understand business needs and to create effective technical solutions.
  • Deploy new Hadoop infrastructure, Hadoop cluster upgrades, cluster maintenance, troubleshooting, capacity planning and resource optimization.
  • Maintain dependency plans between planned sprints across engineering, infrastructure, and third parties.
  • Direct: design, develop, and maintain end to end data solutions using open source, modern data lake, and enterprise data warehouse technologies Hadoop, spark, cloud, etc.

 

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