EventQL from 10,000 feetA quick introduction to the EventQL architecture.
Efficient Data Storage for Analytics with Parquet 2.0 - Hadoop Summit 2014
Efficient Data Storage for Analytics with Parquet 2.0 - Hadoop Summit 2014 Apache Parquet is an open-source columnar storage format for efficient data storage and analytics. It provides efficient compression and encoding techniques that enable fast scans and queries of large datasets. Parquet 2.0 improves on these efficiencies through techniques like delta encoding, dictionary encoding, run-length
Cloudera Blog
We are thrilled to announce the general availability of the Cloudera AI Inference service, powered by NVIDIA NIM microservices, part of the NVIDIA AI Enterprise platform, to accelerate generative AI deployments for enterprises. This service supports a range of optimized AI models, enabling seamless and scalable AI inference. Background The generative AI landscape is evolving […] Read blog post
PostgreSQL9.3をカラム指å‘ストレージ(cstore_fdw)ã«å¯¾å¿œã•ã›ã‚‹
分æžå‘ã‘データベースを展開ã—ã¦ã„ã‚‹ CitusDB ㌠PostgreSQL を列指å‘ストレージ対応ã•ã›ã‚‹ foreign data wrapper(cstore_fdw) をオープンソース化ã—ãŸã®ã§ã€ã¨ã‚Šã‚ãˆãšã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã—ã¦ã¿ãŸã€‚ cstore_fdw ã®ç‰¹å¾´ github ã® cstore_fdw ã«ç‰¹å¾´ãŒã¾ã¨ã‚られã¦ã„る。 http://citusdata.github.io/cstore_fdw/ 箇æ¡æ›¸ãã™ã‚‹ã¨ Faster Analytics – Reduce analytics query disk and memory use by 10x Lower Storage – Compress data by 3x Easy Setup – Deploy as standard PostgreSQL extension Flexibility – Mix row- and c
A tour through hybrid column/row-oriented DBMS schemes
There has been a lot of talk recently about hybrid column-store/row-store database systems. This is likely due to many announcements along these lines in the past month, such as Vertica’s recent 3.5 release which contained FlexStore, Oracle’s recent revelation that Oracle Database 11g Release 2 uses column-oriented storage for the purposes of superior compression, and VectoreWise’s recent decloaki
Who is How Columnar? Exadata, Teradata, and HANA – Part 1: Column Compression
There are three forms of columnar-orientation currently deployed by database systems today. Each builds upon the next. The simplest form uses column-orientation to provide better data compression. The next level of maturity stores columnar data in separate structures to support columnar projection. The most mature implementations support a columnar database engine that performs relational algebra
Parquet Hadoop Summit 2013
Parquet is a columnar storage format for Hadoop data. It was developed by Twitter and Cloudera to optimize storage and querying of large datasets. Parquet provides more efficient compression and I/O compared to traditional row-based formats by storing data by column. Early results show a 28% reduction in storage size and up to a 114% improvement in query performance versus the original Thrift form
A BILLION ROWS PER SECOND Metaprogramming Python for Big Data
Ville Tuulos Principal Engineer @ AdRoll ville.tuulos@adroll.com We faced the key technical challenge of modern Business Intelligence: How to query tens of billions of events interactively? Our solution, DeliRoll, is implemented in Python. Everyone knows that Python is SLOW. You can't handle big data with low latency in Python! Small Benchmark Data: 1.5 billion rows, 400 columns - 660GB. Smaller e
Metaprogramming Python for Big Data
For many companies, understanding what is going on in your business involves lots of data. But, how do you query 10s of billions of data points? How can a company begin to make sense of so much information? Ville Tuulos, Principle Engineer at AdRoll, a company producing tons of big data, demonstrates how AdRoll uses Python to squeeze every bit of performance out of a single high-end server. They m
Hadoop Hive - ORC Files
ORC File Format File Structure Stripe Structure HiveQLSyntax Serialization and Compression Integer Column Serialization String Column Serialization Compression ORC File Format The Optimized Row Columnar (ORC) file format provides a highly efficient way to store Hive data. It was designed to overcome limitations of the other Hive file formats. Using ORC files improves performance when Hive is readi
Byte-dictionary encoding - Amazon Redshift
In byte dictionary encoding, a separate dictionary of unique values is created for each block of column values on disk. (An Amazon Redshift disk block occupies 1 MB.) The dictionary contains up to 256 one-byte values that are stored as indexes to the original data values. If more than 256 values are stored in a single block, the extra values are written into the block in raw, uncompressed form. Th
Text255 and Text32k encodings - Amazon Redshift
Text255 and text32k encodings are useful for compressing VARCHAR columns in which the same words recur often. A separate dictionary of unique words is created for each block of column values on disk. (An Amazon Redshift disk block occupies 1 MB.) The dictionary contains the first 245 unique words in the column. Those words are replaced on disk by a one-byte index value representing one of the 245
Mostly encoding - Amazon Redshift
Mostly encodings are useful when the data type for a column is larger than most of the stored values require. By specifying a mostly encoding for this type of column, you can compress the majority of the values in the column to a smaller standard storage size. The remaining values that cannot be compressed are stored in their raw form. For example, you can compress a 16-bit column, such as an INT2
Apache HBase I/O - HFile | Cloudera Blog
Introduction Apache HBase is the Hadoop open-source, distributed, versioned storage manager well suited for random, realtime read/write access. Wait wait? random, realtime read/write access? How is that possible? Is not Hadoop just a sequential read/write, batch processing system? Yes, we’re talking about the same thing, and in the next few paragraphs, I’m going to explain to  you how HBase achiev
Delta encoding - Amazon Redshift
Delta encodings are very useful for date time columns. Delta encoding compresses data by recording the difference between values that follow each other in the column. This difference is recorded in a separate dictionary for each block of column values on disk. (An Amazon Redshift disk block occupies 1 MB.) For example, suppose that the column contains 10 integers in sequence from 1 to 10. The firs
グーグルã®BigQueryã€é«˜é€Ÿå‡¦ç†ã®ä»•çµ„ã¿ã¯ã€Œã‚«ãƒ©ãƒ 型データストアã€ã¨ã€Œãƒ„ãƒªãƒ¼æ§‹é€ ã€ã€‚解説文書ãŒå…¬é–‹ ï¼ Publickey
グーグルã®BigQueryã€é«˜é€Ÿå‡¦ç†ã®ä»•çµ„ã¿ã¯ã€Œã‚«ãƒ©ãƒ 型データストアã€ã¨ã€Œãƒ„ãƒªãƒ¼æ§‹é€ ã€ã€‚解説文書ãŒå…¬é–‹ SQLã®ã‚¯ã‚¨ãƒªã«å¯¾å¿œã—ã€3億件を超ãˆã‚‹ãƒ‡ãƒ¼ã‚¿ã«å¯¾ã—ã¦ã‚¤ãƒ³ãƒ‡ãƒƒã‚¯ã‚¹ã‚’使ã‚ãªã„フルスã‚ャン検索ã§10秒以内ã«çµæžœã‚’出ã™ã€‚グーグルã®BigQueryã¯å¤§è¦æ¨¡ãªã‚¯ã‚¨ãƒªã‚’超高速ã§å®Ÿè¡Œã™ã‚‹èƒ½åŠ›ã‚’æä¾›ã™ã‚‹ã‚µãƒ¼ãƒ“スã§ã™ã€‚ãã®å†…部を解説ã™ã‚‹æ–‡æ›¸ã€ŒAn Inside Look at Google BigQueryã€ï¼ˆPDF）を公開ã—ã¾ã—ãŸã€‚ グーグルã¯å¤§è¦æ¨¡ã‚¯ã‚¨ãƒªã‚’実行ã™ã‚‹ã‚µãƒ¼ãƒ“スã¨ã—ã¦ç¤¾å†…ã§ã‚³ãƒ¼ãƒ‰ãƒãƒ¼ãƒ 「Dremelã€ã‚’構築ã—ã¦ãŠã‚Šã€2010å¹´ã«ãã®Dremelを解説ã™ã‚‹æ–‡æ›¸ã€ŒDremel: Interactive Analysis of Web-Scale Datasetsã€ã‚’公開ã—ã¦ã„ã¾ã™ã€‚BigQueryã¯ã€ãã®Dremelを外部公開å‘ã‘ã«å®Ÿè£…ã—ãŸã‚‚ã®ã§ã™ã€‚ グーグルã¯ã“ã®Dremel/BigQue
Dremel made simple with Parquet
Columnar storage is a popular technique to optimize analytical workloads in parallel RDBMs. The performance and compression benefits for storing and processing large amounts of data are well documented in academic literature as well as several commercial analytical databases. The goal is to keep I/O to a minimum by reading from a disk only the data required for the query. Using Parquet at Twitter,
Cloudera Blog
The ongoing progress in Artificial Intelligence is constantly expanding the realms of possibility, revolutionizing industries and societies on a global scale. The release of LLMs surged by 136% in 2023 compared to 2022, and this upward trend is projected to continue in 2024. Today, 44% of organizations are experimenting with generative AI, with 10% having […] Read blog post
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Parquet - Data I/O - Philadelphia 2013
GitHub - metamx/druid: Real²time Exploratory Analytics on Large Datasets
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