BigData

Apache Hadoop is free open Source software for massive distributed computation and storage capabilities.

Hadoop can store Peta Bytes of data and process it very fast.

Hadoop does it using cluster of many commodity servers (nodes) where each data node store portion of the data and used as compute node to process its local data. Hadoop echo systems use Map Reduce model to be able to compute huge amount of data in great speed. The Map do the filtering and sorting in local node and reduce perform the summary operation on all nodes output .

Hadoop storage layer is based on HDFS – Hadoop Distributed file system. Where Hadoop split files into large blocks and distribute them between the cluster data nodes.
HDFS storage have following advantages:

  1. Storage is shared between all data nodes and HDFS clients.
  2. high availability – during writes data blocks are replicated to other nodes as well so in case of failure the data is still available for read and writes on other nodes.
  3. Scalable – Scaling out by adding more data nodes storage and processing capacity grow accordingly.
  4. Performance more data nodes more IO throughput the cluster produce.

Hadoop ecosystem can be extended  with sub modules , software packages that can be installed on top or alone side Hadoop , such as Apache Hive , Apache HBase , Apache Spark , Impala and so On .

Hadoop comes today in 2 main distributions which can be free or licensed:

  1. Cloudera – Distribution Including Apache Hadoop (CDH)
  2. Hortonworks Data Platform – HDP

 

Hortonworks and Cloudera have been merged in 2019.

Use Case:
Hadoop is great solution for on premise Data warehouse or data lakes where data is stored in batches and fast analytics on huge data is required + the free license.

 

Our Services:

  • Big Data Architecture
  • Hadoop DevOps: Cluster Installation, Performance Tuning, Upgrades, backup and recovery
  • Data Engineering using Python and PySpark.
  • ETL and Analytics Development using Impala and Spark
  • 24*7 Support

Elasticsearch is free open source search engine based on Apache Lucene with many goodies such as aggregation, analytics, ETL using Logstash and Kibana dashboard which make it today the best search engine in world with many capabilities to become centralized database for many products and companies.

Elasticsearch can store Peta bytes of data on many servers (nodes) and being searched and analyzed in very high speed.

Elasticsearch is great database for unstructured data, as data is being stored in Json format enabling auto schema detection plus adding fields on the fly with no schema modification.

By default, every field is being indexes and can be searched with best response time.

No More need to Relational Database RDBMS with tables and joins, data can be stored as application Json object, inserted and queried much faster in json format, protocol the application can understand .

Elasticsearch support scale out by adding nodes and high availability using replication between the nodes.

Use Case:
Elasticsearch is great database to store real time structured and unstructured data with very fast queries and free text search.

 

Our Services:

  • Big Data Architecture
  • Elasticsearch DevOps – Cluster Installation, performance tuning, backup and recovery.
  • Data Engineering Using Python and PySpark
  • Application development using Python ad Node JS.
  • 24*7 Support

Apache Cassandra is free open source distributed wide column store initially developed by Facebook.

Cassandra is master less – no need to define master node, every node writes and read its local shard data bringing best high availability and speed .

Cassandra belong to the NoSQL family but has CQL (SQL Like ) Interface which enable you to create tables , primary keys , cluster indexes and write “SQL” to insert and select data .Cassandra Distribute the data between the nodes according to the primary key values thus load balance the read /writes requests between the nodes evenly .

The fact that Cassandra have cluster indexes enable the application to retrieve data very fast by key value but also to make a very fast drill down to the cluster index columns which is not avaiable in other NoSQL databases.

Cassandra support scale out by adding nodes and high availability using replication between the nodes.

Cassandra can support 1 million Operation per second and very heavy read write operations in real time.

Use Cases

Application types: Social networks, Time series, IOT, session management.

Our Services:

  • Big Data Architecture and Cassandra data modeling
  • Cassandra DevOps – Cluster Installation, performance tuning, backup and recovery.
  • Data Engineering Using Python and PySpark
  • Application development using Python ad Node JS.
  • 24*7 Support

Google BigQuery is a fast, powerful, flexible, and cost-effective serverless data warehouse that’s tightly integrated with the other services on a Google Cloud Platform. Designed to help you make informed decisions quickly, the cloud-based data warehouse and analytics platform uses a built-in query engine and a highly scalable serverless computing model to process terabytes of data in seconds and petabytes in minutes.

BigQuery is a fully-managed data warehouse on RESTful web service that enables highly scalable, cost-effective and fast analysis of big data

The database allows users to create and delete tables based on a JSON-encoded schema, import data encoded as CSV or JSON from Google Storage. Queries are expressed in a standard SQL dialect and the results are returned in JSON with a maximum reply length of approximately 128 MB, or an unlimited size when large query results are enabled. 

Google BigQuery can process and run reports on real-time data by leveraging other GCP services and resources. Data warehouses can support analytics after data from multiple sources is consolidated and stored — which often happens in batches throughout the day. In addition to batch processing, the service supports streaming at a rate of millions of rows of data per second.

Use Cases:

High End Big Data Platform for Data Warehouse and analytic database

Our Services:

Data Architecture , Data Modeling , Data Engineering and Development , Data Analysis

 

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 (Simple Storage Service), using standard SQL. Since Athena is serverless, there is no infrastructure to manage, and you pay only for the queries that you run.

Athena relies on the open source Presto distributed SQL query engine to enable both quick ad-hoc analysis and more complex requests, including window functions, large joins and aggregations

Amazon Athena is able to process both unstructured and structured data types, data can be stored in the form of CSV, JSON or columnar data formats like Apache Parquet and Apache ORC. Athena can also be used to execute queries using ANSI SQL, which doesn’t require the user to aggregate or load data into Athena service.  Compressed data is also supported in Snappy, Zlib, LZO and GZIP formats.

The service provides great flexibility in how you run queries without the added complexity. Multiple concurrent queries can run all at the same time and most results are delivered within seconds. These actionable results from queries allow companies access to clean, reliable data to make better decisions and continue their research.

Athena also integrates with sophisticated BI tools like Tableau, Looker, Mode Analytics, AWS QuickSight, and others for advanced reports and visualizations.

Use Cases:

Big Data Platform for Data Warehouse and analytic database

 

Our Services:

 

Data Architecture, Data Modeling , Data Engineering and Development , Data Analysis 

Presto is an open-source, distributed SQL query engine that runs on Hadoop. It uses an architecture similar to a classic massively parallel processing (MPP) database management system and was designed for fast analytic queries against data of any size. 

The system was initially designed at Facebook as they needed to run interactive queries against large data warehouses in Hadoop. It was explicitly designed to fill the gap/need to be able to run fast queries against data warehouses storing petabytes of data. 

Presto supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), Amazon S3, Cassandra, MongoDB, and HBase, and relational data sources such as MySQL, PostgreSQL, Amazon Redshift, Microsoft SQL Server, and Teradata.

Presto has one coordinator node working in sync with multiple worker nodes. Users submit their SQL query to the coordinator which uses a custom query and execution engine to parse, plan, and schedule a distributed query plan across the worker nodes. 

It is designed to support standard ANSI SQL semantics, including complex queries, aggregations, joins, left/right outer joins, sub-queries, window functions, distinct counts, and approximate percentiles.

Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds. 

Use Cases:

Big Data Platform for Data Warehouse and analytic database

Our Services:

Data Architecture, Data Modeling , Data Engineering and Development , Data Analysis 

Exasol is a high performance parallelized relational database management system (RDBMS) which runs on a cluster of standard computer hardware servers. 

This database is designed to run in memory, although data is persistently stored on disk following the ACID rules. Exasol supports the SQL Standard 2003 and can be integrated via standard interfaces like ODBC, JDBC or ADO.NET.

Exasol has implemented a so-called cluster operating system (EXACluster OS). It is based on standard Linux and provides a runtime environment and storage layer for the RDBMS, employing a proprietary, cluster-based file system (ExaStorage).

This analytics gives you unparalleled performance at scale, processing 100’s of TBs of data within milliseconds.

Exasol consolidates AI, ML and BI for both standard and advanced analytics, directly in the database – using any data science language.

Use Cases:

Big Data Platform for Data Warehouse and analytic database

Our Services:

Data Architecture , Data Modeling , Data Engineering and Development , Data Analysis 

Vertica is an elastically scalable, advanced SQL analytics database purpose built to manage rapidly growing volumes of data, maximizing cloud economics for mission-critical big data analytics initiatives. 

It is designed for use in data warehouses and other big data workloads where: speed, scalability, simplicity, and openness are crucial to the success of analytics as well including full integration with an ecosystem of tools and technologies.

Vertica relies on a tested, reliable distributed architecture and columnar compression to deliver lightning fast speed with full ANSI SQL compliance. 

The system runs on multiple cloud platforms (AWS, Google, Azure), on-premises, and seamlessly integrates within an existing data pipeline consisting of Kafka, Spark, and/or Hadoop for a comprehensive data warehouse solution.

Use Cases:

Big Data Platform for Data Warehouse and analytic database

Our Services:

Data Architecture , Data Modeling , Data Engineering and Development , Data Analysis 

Redshift is a fully managed, cloud-based big data warehouse service offered by Amazon. 

The platform provides a storage system that stores petabytes of data in easy-to-access clusters that can be queried in parallel. Each of these nodes can be accessed independently by users and applications. 

Companies can choose to create a single node as a starting point, and from there they can create massive clusters containing many nodes for every reporting need they have for any web application.

Redshift is designed to be used with a variety of data sources and data analytics tools and is compatible with several existing SQL-based clients.

 

Amazon Redshift is a powerhouse of data warehousing. Used for large scale and complex analytics by some of the largest companies in the world, including Ford Motor Company, Lyft, Intuit, and Pfizer plus countless more, the data warehouse is used to store cloud databases and the related production data.

Use Cases:

Big Data Platform for Data Warehouse and analytic database

Our Services:

Data Architecture , Data Modeling , Data Engineering and Development , Data Analysis

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