Transcription
Data Lake ImplementationWhat is the serviceMost of the enterprises have their data present in silos. Due to this, the data that is available to thedecision-makers is always either incomplete or inaccurate.Also, the data presented to the decision-makers do not cater to the data that is unstructured i.e.PDF’s, docs, emails, social media etc.This has a huge impact on the decision taken by the higher management.A data lake is a centralized repository that allows storage all structured and unstructured data atany scale. Organizations can store your data as-is, without the need to structure the data, and thenrun different types of analytics for decision making.
How data lakeimplementationcan helpIn bringing data from allsource systems at a singleplace which ultimatelyleads to the removal ofdata silos.All kinds of data areavailable to the decisionmakers i.e. structured,semi-structured andunstructured.The traditional approach- decide on a use caseand then collect therequired dataRather than having togo to multiple sourcesystems for data,all data is presentat 1 placeData Lake approach- you always collectall the available datairrespective of the usecase
Our ServicesConsulting and roadmap discussion and creation forenterprise data lakeDesigning data lake layered architecture solutionskeeping in mind the end user reporting and dashboardrequirements as well as analytics and AI related use cases.Setting up data lake using Big data and cloudtechnologies like hadoop, AWS, Azure etcUnstructured data extraction (web, log files, social media,pdfs, etc.) from external sources and data parsing usingNLP techniques and machine learning algorithms.Frameworks for ingestion and analysis of real timeand streaming data from IOT devices, logs etc usingbig data technologies like Kafka, Spark Streaming,Streamsets etcEnterprise Search, discover and analysiscapabilities on the data lake usingtechnologies like SOLR and ELK stack.Data modelling based on industry standardbusiness specific models for the data lakeImplementation of metadata and masterdata management, data quality anddata governance on the data lakeImplementation of ETL tools like Talend,Informatica as well as schedulers likeControl-M as tools for integrating,orchestrating and scheduling the datalake processes.Implementation of data lake security andgovernance, for on-premise and on-cloudimplementations.Industry standards, Best practices, referencearchitectures and success stories for thedata lake available
Case StudiesEllicium implemented DataLake on Azure cloud using BigData, for one of the leading UShealthcare chains.Ellicium implemented data lakefor a leading Insurance giantenabling faster and rich dataanalytics.
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capabilities on the data lake using technologies like SOLR and ELK stack. Data modelling based on industry standard business specific models for the data lake Implementation of metadata and master data management, data quality and data governance on the data lake Implementation of ETL tools like Talend, Informatica as well as schedulers like
