The columnar roadmap: Apache Parquet and Apache ArrowDataWorks Summit
The Hadoop ecosystem has standardized on columnar formats—Apache Parquet for on-disk storage and Apache Arrow for in-memory. With this trend, deep integration with columnar formats is a key differentiator for big data technologies. Vertical integration from storage to execution greatly improves the latency of accessing data by pushing projections and filters to the storage layer, reducing time spent in IO reading from disk, as well as CPU time spent decompressing and decoding. Standards like Arrow and Parquet make this integration even more valuable as data can now cross system boundaries without incurring costly translation. Cross-system programming using languages such as Spark, Python, or SQL can becomes as fast as native internal performance.
In this talk we’ll explain how Parquet is improving at the storage level, with metadata and statistics that will facilitate more optimizations in query engines in the future. We’ll detail how the new vectorized reader from Parquet to Arrow enables much faster reads by removing abstractions as well as several future improvements. We will also discuss how standard Arrow-based APIs pave the way to breaking the silos of big data. One example is Arrow-based universal function libraries that can be written in any language (Java, Scala, C++, Python, R, ...) and will be usable in any big data system (Spark, Impala, Presto, Drill). Another is a standard data access API with projection and predicate push downs, which will greatly simplify data access optimizations across the board.
Speaker
Julien Le Dem, Principal Engineer, WeWork
Cassandra concepts, patterns and anti-patternsDave Gardner
An introduction to the fundamental concepts behind Apache Cassandra. This talk explains the engineering principles that make Cassandra such an attractive choice for building highly resilient and available systems and then goes on to explain how to use it - covering basic data modelling patterns and anti-patterns.
In this exclusive Premier Inside Out, you will hear from Druid committer Slim Bouguerra, Staff Software Engineer and Product Manager Will Xu. These Hortonworkers will explain the vision of these components, review new features, share some best practices and answer your questions.
View the webinar here: https://hortonworks.com/webinar/hortonworks-premier-apache-druid/
The columnar roadmap: Apache Parquet and Apache ArrowDataWorks Summit
The Hadoop ecosystem has standardized on columnar formats—Apache Parquet for on-disk storage and Apache Arrow for in-memory. With this trend, deep integration with columnar formats is a key differentiator for big data technologies. Vertical integration from storage to execution greatly improves the latency of accessing data by pushing projections and filters to the storage layer, reducing time spent in IO reading from disk, as well as CPU time spent decompressing and decoding. Standards like Arrow and Parquet make this integration even more valuable as data can now cross system boundaries without incurring costly translation. Cross-system programming using languages such as Spark, Python, or SQL can becomes as fast as native internal performance.
In this talk we’ll explain how Parquet is improving at the storage level, with metadata and statistics that will facilitate more optimizations in query engines in the future. We’ll detail how the new vectorized reader from Parquet to Arrow enables much faster reads by removing abstractions as well as several future improvements. We will also discuss how standard Arrow-based APIs pave the way to breaking the silos of big data. One example is Arrow-based universal function libraries that can be written in any language (Java, Scala, C++, Python, R, ...) and will be usable in any big data system (Spark, Impala, Presto, Drill). Another is a standard data access API with projection and predicate push downs, which will greatly simplify data access optimizations across the board.
Speaker
Julien Le Dem, Principal Engineer, WeWork
Cassandra concepts, patterns and anti-patternsDave Gardner
An introduction to the fundamental concepts behind Apache Cassandra. This talk explains the engineering principles that make Cassandra such an attractive choice for building highly resilient and available systems and then goes on to explain how to use it - covering basic data modelling patterns and anti-patterns.
In this exclusive Premier Inside Out, you will hear from Druid committer Slim Bouguerra, Staff Software Engineer and Product Manager Will Xu. These Hortonworkers will explain the vision of these components, review new features, share some best practices and answer your questions.
View the webinar here: https://hortonworks.com/webinar/hortonworks-premier-apache-druid/
This deck presents the best practices of using Apache Hive with good performance. It covers getting data into Hive, using ORC file format, getting good layout into partitions and files based on query patterns, execution using Tez and YARN queues, memory configuration, and debugging common query performance issues. It also describes Hive Bucketing and reading Hive Explain query plans.
Optimizing Delta/Parquet Data Lakes for Apache SparkDatabricks
This talk outlines data lake design patterns that can yield massive performance gains for all downstream consumers. We will talk about how to optimize Parquet data lakes and the awesome additional features provided by Databricks Delta. * Optimal file sizes in a data lake * File compaction to fix the small file problem * Why Spark hates globbing S3 files * Partitioning data lakes with partitionBy * Parquet predicate pushdown filtering * Limitations of Parquet data lakes (files aren't mutable!) * Mutating Delta lakes * Data skipping with Delta ZORDER indexes
Speaker: Matthew Powers
How the new operation of Hadoop Distributed FIle System (HDFS) -- Append works. The internals of the processing. The new states that are more than the write operation.
This presentation describes how to efficiently load data into Hive. I cover partitioning, predicate pushdown, ORC file optimization and different loading schemes
InfluxDB IOx Tech Talks: Query Processing in InfluxDB IOxInfluxData
Query Processing in InfluxDB IOx
InfluxDB IOx Query Processing: In this talk we will provide an overview of Query Execution in IOx describing how once data is ingested that it is queryable, both via SQL and Flux and InfluxQL (via storage gRPC APIs).
This deck presents the best practices of using Apache Hive with good performance. It covers getting data into Hive, using ORC file format, getting good layout into partitions and files based on query patterns, execution using Tez and YARN queues, memory configuration, and debugging common query performance issues. It also describes Hive Bucketing and reading Hive Explain query plans.
Optimizing Delta/Parquet Data Lakes for Apache SparkDatabricks
This talk outlines data lake design patterns that can yield massive performance gains for all downstream consumers. We will talk about how to optimize Parquet data lakes and the awesome additional features provided by Databricks Delta. * Optimal file sizes in a data lake * File compaction to fix the small file problem * Why Spark hates globbing S3 files * Partitioning data lakes with partitionBy * Parquet predicate pushdown filtering * Limitations of Parquet data lakes (files aren't mutable!) * Mutating Delta lakes * Data skipping with Delta ZORDER indexes
Speaker: Matthew Powers
How the new operation of Hadoop Distributed FIle System (HDFS) -- Append works. The internals of the processing. The new states that are more than the write operation.
This presentation describes how to efficiently load data into Hive. I cover partitioning, predicate pushdown, ORC file optimization and different loading schemes
InfluxDB IOx Tech Talks: Query Processing in InfluxDB IOxInfluxData
Query Processing in InfluxDB IOx
InfluxDB IOx Query Processing: In this talk we will provide an overview of Query Execution in IOx describing how once data is ingested that it is queryable, both via SQL and Flux and InfluxQL (via storage gRPC APIs).
Building a distributed Key-Value store with Cassandraaaronmorton
Slides from my talk at Kiwi Pycon in 2010.
Covers why we chose Cassandra, overview of it's feature and data model, and how we implemented our application.
As a reformed CQL critic, I'd like to help dispel the myths around CQL and extol its awesomeness. Most criticism comes from people like me who were early Cassandra adopters and are concerned about the SQL-like syntax, the apparent lack of control, and the reliance on a defined schema. I'll pop open the hood, showing just how the various CQL constructs translate to the underlying storage layer--and in the process I hope to give novices and old-timers alike a reason to love CQL.
Friction-free ETL: Automating data transformation with Impala | Strata + Hado...Cloudera, Inc.
Speaker: Marcel Kornacker
As data is ingested into Apache Hadoop at an increasing rate from a diverse range of data sources, it is becoming more and more important for users that new data be accessible for analysis as quickly as possible—because “data freshness” can have a direct impact on business results.
In the traditional ETL process, raw data is transformed from the source into a target schema, possibly requiring flattening and condensing, and then loaded into an MPP DBMS. However, this approach has multiple drawbacks that make it unsuitable for real-time, “at-source” analytics—for example, the “ETL lag” reduces data freshness, and the inherent complexity of the process makes it costly to deploy and maintain, and reduces the speed at which new analytic applications can be introduced.
In this talk, attendees will learn about Impala’s approach to on-the-fly, automatic data transformation, which in conjunction with the ability to handle nested structures such as JSON and XML documents, addresses the needs of at-source analytics—including direct querying of your input schema, immediate querying of data as it lands in HDFS, and high performance on par with specialized engines. This performance level is attained in spite of the most challenging and diverse input formats, which are addressed through an automated background conversion process into Parquet, the high-performance, open source columnar format that has been widely adopted across the Hadoop ecosystem.
In this talk, attendees will learn about Impala’s upcoming features that will enable at-source analytics: support for nested structures such as JSON and XML documents, which allows direct querying of the source schema; automated background file format conversion into Parquet, the high-performance, open source columnar format that has been widely adopted across the Hadoop ecosystem; and automated creation of declaratively-specified derived data for simplified data cleansing and transformation.
SQL stands for Structured Query Language
SQL lets you access and manipulate databases
SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987
Creating and Managing Tables -Oracle Data base Salman Memon
After completing this lesson, you should be able to
do the following:
Describe the main database objects
Create tables
Describe the data types that can be used when specifying column definition
Alter table definitions
Drop, rename, and truncate tables
http://phpexecutor.com