Meanwhile, AWS s data lake – Elastic Map Reduce – can handle SQL queries via SQL Workbench or Presto SQL. Azure supports SQL queries in its data lakes (HD Insight or Azure Databricks) while GCP uses a combination of Bigtable, Dataflow and Bigquery.
But these implementations are not able to handle the number of SQL queries supported by traditional data warehouses, some of which can scale to thousands of concurrent users. Latency and concurrency an issue
Pal told the Gartner Data & Analytics Summit: Data lakes are actually not being used for BI workloads, especially in large organizations that need high concurrency, as well as low latency. The SQL engines that have been developed on the data lakes have never really been able to keep up with the concurrency and latency requirements.
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Summary
The COVID-19 pandemic wreaked havoc on supply chains worldwide and posed a critical threat to multinational manufacturers like Henkel AG & Co., which depend on the reliable delivery of parts, chemicals, and other goods to supply their factories. This case study explores the challenges Henkel AG faced and how it transformed its data operation and deployed accessible BI tools and dashboards to dynamically manage its supply chain operations.
Company: Henkel AG & Co. KGaA is an international chemical and consumer goods company with its headquarters in Düsseldorf, Germany. It is a multinational company active both in the consumer and industrial sector. Its brands include Persil, Right Guard, Sil, Loctite, and Schwarzkopf. Turnover is roughly $20 billion per year. Henkel is a member of the IoT Consortium (joined January 2019).