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Spark-Powered ETL

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).

 

 

Spark-Powered ETL/ELT solutions

This solution model is designed to be deoployed both on Cloud(AWS and Azure) and on-premises.

Spark Scala based engine powers the ETL/ELT component of this model,providing interfaces to different data sources such as JDBC/ODBC based interface for RDBMS ( Oracle, MySQL, PostgreSQL, MS-SQL Server, AWS RDS), ElasticSearch, SFTP/FTP Servers, Logs from web servers, CSV/TSV, text files and other sources.

Spark with hadoop platform and other supporting interfaces for AWS S3, Google FileSystem and Azure storages.

Benefits:

          High performances as it is powered by Spark Engine.

          Existing spark engine can be plugged in with this model

          Single data processing platform for majority of the data preparation and processing along cutomizable interfaces to any target such as other hadoop, No-SQL data store(MongoDB, HBase, Cassandara, ElasticSearch and Hive).