SparklineData( Now part of Oracle)

Fast B.I and Analytics and OLAP on Spark

[Cindi Howson @BIScorecard]

Just published! Updated review of @tableau strengths & weaknesses w @rsallam #GartnerDA https://t.co/ReRZo3ZRWE?

[SparklineData( Now part of Oracle) @SparklineData]

@tableau @awscloud @OracleAnalytics @OracleDataCloud @ApacheSpark Data and Analytics 2018. Age of Spark @databricks https://t.co/HKcLnfWbGG

[Sri Desikan @ohzoneD]

Data and Analytics predictions for 2018 - https://t.co/8dO4U1mfzB #spark #fastbi #analytics #businessintelligence

[SparklineData( Now part of Oracle) @SparklineData]

Speed matters. https://t.co/LBKZ3MAq9T

[Sri Desikan @ohzoneD]

Data warehouse optimization remains the top use case for #BigData #analytics. #EnSW https://t.co/45b1KxKwoT
#fastbi on the fastest DW @SparklineData

[Francois Ajenstat @Ajenstat]

Great Q&A with @elissafink on helping Marketers Make More Data-Driven Decisions About Growth. https://t.co/iVrNrtPDgd @tableau via @Forbes

[SparklineData( Now part of Oracle) @SparklineData]

Yes! We have been preaching this at SparklineData for a few years now. https://t.co/FZEmpA9SUc

[Julian Hyde @julianhyde]

Here's my point:
1. Materialized views are an amazing way to speed up analytics;
2. MVs come in many forms, not always CREATE MATERIALIZED VIEW.
3. MVs are an ancient technique (think CREATE INDEX);
4. MVs are ever more important as data becomes heterogenous and distributed.

[Francois Ajenstat @Ajenstat]

An early holiday present - @tableau #projectmaestro beta 2 has been released. Lots of new stuff - Pivot, Wild card union, Data interpreter, Aggregate and Join Improvements, Write to CSV, Rename Nodes, Improvements to expression editor, nGram algorithm in fuzzy clustering, +more

[Naval @naval]

Tulips are not durable, not scarce, not programmable, not fungible, not verifiable, not divisible, and hard to transfer. But tell me more about your analogy...

[SparklineData( Now part of Oracle) @SparklineData]

I just published “Building a fast datamart on Redshift” https://t.co/BQWAb2BZdl

[SparklineData( Now part of Oracle) @SparklineData]

I just published “Last mile analysis and modern B.I” https://t.co/TMnSBjhtT2

[SparklineData( Now part of Oracle) @SparklineData]

Are you limited in your ability to be data driven? Combine #bi and #ai Democratize analytics with SparklineSNAP #fastbi https://t.co/usfYFaYhxv

[Sri Desikan @ohzoneD]

All analytics is fundamentally about counting. Know what to count(metrics) and count fast you are half way there #fastbi @SparklineData

[SparklineData( Now part of Oracle) @SparklineData]

I just published “In a typical enterprise there are broadly two kinds of B.I projects .” https://t.co/tzOcevoPZh

[SparklineData( Now part of Oracle) @SparklineData]

My biggest pain with BI is

[SparklineData( Now part of Oracle) @SparklineData]

Analyzing data in seconds with Python, SparklineSNAP and @nteractio Exploratory data analysis and drag and drop B.I can exist side by side. See how you can multi-dimensionally explore a 100 million row dataset. https://t.co/H66TR0t3Ah #spark #sparksql #python #fastbi

[SparklineData( Now part of Oracle) @SparklineData]

I just published “Enterprise B.I in the age of A.I” https://t.co/dqvmghj3PT

[Hasjob: Hasgeek Jobs @hasjob]

Software Engineer: Spark, SQL, Big Data Analytics https://t.co/xwAZTubxrh #Bangalore @sparklinedata

[Julian Hyde @julianhyde]

Never bet against standard SQL https://t.co/ybVBovj2ZT

[SparklineData( Now part of Oracle) @SparklineData]

#apachespark is more than datascience. Powered by SparkSQL companies are deriving ROI from spark based B.I #fastbi https://t.co/p5veCAhNSz

[Tableau @tableau]

We're excited to be #8 on the #FortuneFuture50 list! 🎉 https://t.co/0CAUosr2yl