Do you know about KGB Archiver, it is one of the best tools for compressing large files into small files. It can be used to compress even a 1GB file into 10MB, which is not possible. Using this tool you can save more space on your disks. This tool is recently found to save data and space.
While transferring large files like movies and Games it will take more time but this tool completely changes when we talk about large files. There is a Solution to solve this problem it can be used to compress data into any format like .zip Rar, and uploading them into online (for sharing). In this method whenever you want to share them you can easily download and extract your data.So now, you can fully do that using KGB Archiver tool. Follow this guide in this article and learn more about how to highly compress files using KGB Archiver to Compressor and upload them into online to save your data balance.
Here the tool KGB achieve is a tool which makes your files into smaller and we can compress the files into smaller and may save your memory my sincere advice is to use and may reduce your disc space efficiently without any problems, I think this a cool and efficient tool which will reduce your disc space, and the files are safe.and we can easily back up the data required.
after download, we need to take the following the steps and read the steps while you perform. dont worry about the installation process I am there to help you and after installing we get a pop up window if not go to start and search for the KGB achiever and open the file and we get a pop-up window and in the menu we have to choose the file to be compressed and then type of compression level you need ,the compression level may decrease the size of the file and may advise is to the high compression level. after that go for compress button and wait for few minutes.
The time is mainly depend on the level of compression and the performance of our PC like maximum ,normal,low,very weak and after completion we get the compressed file and in this way we can easily convert the files and compress the files easily by using the KBG archive so I think that u get the usage and advantages and disadvantages of the tool so please be careful and enjoy by this KGB archiver thanks for being patience
To get started, you create a workgroup that will allow you to specify your query engine, your working directory in Amazon Simple Storage Service (S3) to hold the results of your execution, AWS Identity and Access Management (IAM) roles (if needed), and your resource tags. You can use workgroups to separate users, teams, applications, or workloads; set limits on the amount of data that each query or the entire workgroup can process; and track costs. Based on the workgroup that you create, you can either (a) run SQL-based queries and get charged for the number of bytes scanned or (b) run Apache Spark Python code and get charged an hourly rate for executing your code.
You are charged based on the amount of data scanned by each query. You can get significant cost savings and performance gains by compressing, partitioning, or converting your data to a columnar format because each of those operations reduces the amount of data that Athena needs to scan to run a query.
You are charged for the number of bytes that Athena scans, rounded up to the nearest megabyte, with a 10MB minimum per query. There are no charges for Data Definition Language (DDL) statements like CREATE/ALTER/DROP TABLE, statements for managing partitions, or failed queries. Canceled queries are charged based on the amount of data scanned.
Compressing your data allows Athena to scan less data. Converting your data to columnar formats allows Athena to selectively read only required columns to process the data. Athena supports Apache ORC and Apache Parquet. Partitioning your data also allows Athena to restrict the amount of data scanned. This leads to cost savings and improved performance. You can see the amount of data scanned per query on the Athena console. For details, see the pricing example below.
You only pay for the time that your Apache Spark application takes to run. You are charged an hourly rate based on the number of data processing units (DPUs) used to run your Apache Spark application. A single DPU provides 4 vCPU and 16 GB of memory. You will be billed in increments of 1 second, rounded up to the nearest second.
Athena queries data directly from Amazon S3. There are no additional storage charges for querying your data with Athena. You are charged standard S3 rates for storage, requests, and data transfer. By default, query results are stored in an S3 bucket of your choice and are also billed at standard S3 rates.
This article was co-authored by Luigi Oppido and by wikiHow staff writer, Nicole Levine, MFA. Luigi Oppido is the Owner and Operator of Pleasure Point Computers in Santa Cruz, California. Luigi has over 25 years of experience in general computer repair, data recovery, virus removal, and upgrades. He is also the host of the Computer Man Show! broadcasted on KSQD covering central California for over two years.There are 11 references cited in this article, which can be found at the bottom of the page.The wikiHow Tech Team also followed the article's instructions and verified that they work. This article has been viewed 2,234,895 times.
Storing large files on Windows or macOS can be frustrating when you're low on disk space. It can also be tricky to share and send large files to others because of email and text size limitations. Fortunately, there are several tools that make it easy to make larger files small enough to share and store. This wikiHow article will teach you how to compress large files, including apps, audio, and video, to much smaller sizes.
Did You Know Compressing a file is ideal when you want to make a file smaller or if you need to compress multiple files into a single packet. For instance, if you need to email a 12mb file but your limit is 10mb, you can compress it down to 7mb. Then, the other person can decompress the file to open it.
May 2022: This post was reviewed and updated with more details like using EXPLAIN ANALYZE, updated compression, ORDER BY and JOIN tips, using partition indexing, updated stats (with performance improvements), added bonus tips.
Amazon Athena is an interactive query service that makes it easy to analyze data stored in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL.
This section discusses how to structure your data so that you can get the most out of Athena. You can apply the same practices to Amazon EMR data processing applications such as Spark, Presto, and Hive when your data is stored in Amazon S3. We discuss the following best practices:
Partitioning divides your table into parts and keeps the related data together based on column values such as date, country, and region. Partitions act as virtual columns. You define them at table creation, and they can help reduce the amount of data scanned per query, thereby improving performance. You can restrict the amount of data scanned by a query by specifying filters based on the partition. For more details, see Partitioning data in Athena.
For example, the following table compares query runtimes between a partitioned and non-partitioned table. Both tables contain 74 GB data, uncompressed and stored in text format. The partitioned table is partitioned by the l_shipdate column and has 2,526 partitions.
Another way to partition your data is to bucket the data within a single partition. With bucketing, you can specify one or more columns containing rows that you want to group together, and put those rows into multiple buckets. This allows you to query only the bucket that you need to read when the bucketed columns value is specified, which can dramatically reduce the number of rows of data to read, which in turn reduces the cost of running the query.
Compressing your data can speed up your queries significantly, as long as the files are either of an optimal size (see the next section), or the files are splittable. The smaller data sizes reduce the data scanned from Amazon S3, resulting in lower costs of running queries. It also reduces the network traffic from Amazon S3 to Athena.
You can compress your existing dataset using AWS Glue ETL jobs, Spark or Hive on Amazon EMR, or CTAS or INSERT INTO and UNLOAD statements in Athena. The following is an example script for compressing using AWS Glue:
Queries run more efficiently when data scanning can be parallelized and when blocks of data can be read sequentially. Ensuring that your file formats are splittable helps with parallelism regardless of how large your files may be.
However, if your files are too small (generally less than 128 MB), the execution engine might be spending additional time with the overhead of opening S3 files, listing directories, getting object metadata, setting up data transfer, reading file headers, reading compression dictionaries, and so on. On the other hand, if your file is not splittable and the files are too large, the query processing waits until a single reader has completed reading the entire file. That can reduce parallelism.
One remedy to solve your small file problem is to use the S3DistCP utility on Amazon EMR. You can use it to combine smaller files into larger objects. You can also use S3DistCP to move large amounts of data in an optimized fashion from HDFS to Amazon S3, Amazon S3 to Amazon S3, and Amazon S3 to HDFS.
For example, the following table compares query runtimes between two tables, one backed by a single large file and one by 100,000 small files. Both tables contain approximately 8 GB of data, stored in text format.
Apache Parquet and Apache ORC are popular columnar data s